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@pulsethebabyoctopus
Marine Bio Master list:
Cephalopods:
Wild octopus friends: Marine life Side blog. Main is DragonGoddessofFate. Cephalopod enthusiast and marine biologist Bigfin squid: Marine
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Which shark is the most Friend Shaped?
Whale Shark
Nurse Shark
Tiger Shark
Zebra Shark
Thresher Shark
Swell Shark
Basking Shark
Goblin Shark
Hammerhead Shark
Port Jackson Shark
Shy Shark
Greenland Shark
cuttlefish have three hearts because they love you so much
he loves you
cuttlefish have three hearts because they speak Latin, Greek, and Oscan
Welcome
I get paid to make maps of the ocean. This post is going to haunt me like that “mothers and fuckers of the jury” post haunts that trial attorney
I’m still not free of that phrase…
Ok, lets have a look at this: Over the last few days a new paper in Science has led to MANY discussions in the paleo community and it appears to have breached containment into the wider world, judging by headlines and google results.
"Earliest octopuses were giant top predators in Cretaceous oceans" by Ikegami et. al describes a new beak of the basal octopus genus Nanaimoteuthis. And yeah, it's a whopper! Just look at the specimen next to a giant squid beak in this figure! They also note that...
...beak shows some intense, asymmetrical wear, indicating a lot of hard objects being processed by this beak. On top of that they assign the genus to the group Cirrata (finned octopuses and relatives) instead of Vampyromorpha as it was in past papers. This is were the hard facts end though.
Don't get me wrong: This must have been a huge animal, but I also think that anything beyond this is purely speculative. The authors give a total length of 7 to 19 meters, an enormous range, with an estimated mantle length of max 4.4 m. They base this on the proportions of finned octopuses and other close relatives but I would argue that is just math for the sake of math. We know VERY little about early octopuses. Their beaks are often the only thing preserved and their diversity in the Cretaceous remains murky.
That's the size part, what I have an actual problem with though is the way they deduce behavior, died and even cognition from this fossil. Based on the size, wear and asymmetry they propose that this animal would compete, maybe even hunt large marine reptiles, in a smart way.
That's plain bullshitting in my eyes. Intense wear on a beak suggests this animal would be durophagous, going after armored or hard shelled prey. cracking the bones of marines reptiles feels very contrived and modern day octopuses (that often eat crabs) don't look much different.
The asymmetry of the beaks is an interesting detail but I would NEVER derive an argument for higher cognition from that. Cognitive abilities are next to impossible to grasp from the fossil record even IF you have the brain. Which leaves the question what was this guy doing?
Short answer is: we don't know. As I hopefully illustrated here we have simply too few data points to make any concrete arguments for this animals appearance or lifestyle. HOWEVER
As people pointed out on Discord: crushing shells in an pelagic habitat is something that was a breeze in the Mesozoic. Ammonites in the cretaceous come in many different shapes but also sizes. 50 cm plus species are not rare.
We also know from the Jurassic there were likely other cephalopods that went after ammonites. So if the ammonites grew in the Cretaceous why shouldn't their predators as well? Beyond ammonites the Late Cretaceous also gave rise to a large to gigantic bivales like many inoceramids
This abundance of durophagous prey is also reflected in the predators, large sharks, mosasaurs and even giant chimeras took advantage of this plentiful food source. I therefore think a large ammonite predator is a much more likely niche for Nanaimoteuthis.
In my interpretation I pair the octopus with the giant ammonite Parapuzosia, these animals aren't known from the same localities but their time ranges overlap which makes it plausible to me that these guys, or close relatives, could have met.
Lastly I want to quickly talk about the promotion and reception of this publication. While I don't completely fault the authors for their writing - after all LOOK AT THE MODERN ACADEMIC CLIMATE - I do think it's troubling that the editor's note, the journal itself, immediately evokes the image of the Kraken, a mythological creature, to sell it's new paper. This in combination with Science being a high profile journal makes it feel as if the claims in this publication are standing on more solid ground than they do. This is just my personal opinion but I think this is just bad science communication. It is something that will echo through the online sphere for years to come and does not in any way promote the caution that I would expect when claims like these are presented. Subsequently the ideas and evocative speech of the paper have already spawned a large amount of paleoart that goes for the largest and most speculative sides of it. Again: I think the size estimates in the paper are certainly possible, but I also think a more critical examination of the text is warranted when presented with such incredible claims. I am not here to kill your fun. But I also think that we are maybe looking at something even more interesting that the (at this point) already rather old trope of the mosasaur eating squid. At least to me a giant mollusk eats mollusk world is cooler.
AS ALWAYS, these are simply my opinions on these matters, but I thought there was enough uncritical yay and nay saying about this paper that I felt like it should warrant a reaction. I think the paper describes fascinating material and I eagerly await more!
sometimes i like to copy my own drawings
thinking about diatoms again
microscopic living stars made of glass that eat the sun. and they're all around us. in every body of water. glass sun-eating stars.
I like them a lot. they produce up to half of all earth's oxygen. the air you breathe is thanks to sun-eating stars made of glass. and that's pretty cool.
and you know. like oblongs and triangles and some other bullshit
"sharks are older than trees" is technically true, but TIL that sharks were a marginal group until the mid-Jurassic (the middle of the Dino Age, ~200-150 mya), and all modern shark orders first appear around this time.
Before then, a different group of cartilaginous fish called hybodonts were the big apex predators. How were they different than sharks? Mostly because they had "boney" dorsal spines made out of dentine and enamel. Like teeth are. Like where would that tech have gone, if they'd survived and kept evolving for 200 million more years?
TIL that sharks went thru a mysterious extinction event 19 million years ago, when 95% of populations and 70% of species disappeared. Sediment samples go from having 1 shark fossil for every 5 fish fossils, to 1 shark per 100 fish (does that mean that the ocean used to be like 16% shark??)
And there was no big climate change then, or extinctions of other groups! This research is pretty recent and no one has even an educated guess!
Two effects of this:
All of today's big ocean sharks (Great White, Tiger, Basking, etc.) all evolved from coastal sharks that survived this event. And,
Populations haven't recovered. Sharks as a group are still decimated, a shadow of their former selves for most of the last 200 million years.
science has always been political. what gets studied. what doesnt. who gets to do the studying. on and on and on.
scientists on this post: yuuuup 👍
people who aren't scientists: um actually ☝️
I left research science for a multitude of personal reasons but a big one was that my autistic arse just could not keep up with the politics. Trying to increase your profile and your work's appeal for grant money (both critically important factors for succeeding in science) by carefully tailoring your work focus, grant proposals and papers to attract the interest of various grant committees focused on what research is in fashion and also to try to maximise the number of references to your papers in other people's papers takes so much time and just isn't interesting.
People in the notes are all like "yes sometimes The Government or The Lobbyists remove funding from sciences that might reveal things that they do not want known" and yes yes covid and climate change and vaccine denialism and racism and all that, but it's not just the obvious stuff. When I was working in labs, metagenomics was the next big thing, so EVERYTHING was metagenomics. If you worked in environmental or medical microbio, there was an 80% chance that you were taking some mud or poop or something and doing metagenomics on it whether or not it was the best way to analyse what you wanted to analyse, and bam, that was a paper, possibly two, it kept the lights on. And if 100 labs run 100 experiments where they do metagenomics for random things to do with the human body, then at a 95% confidence interval, 5 of them will find a completely nonexistent correlation for any random thing you can think of if they happen to check their data for it, and they need to keep the lights on and the PhD student who's running the experiment needs a paper so even though that wasn't what they were looking for, bam -- everyone's seeing that men and women have different microbiomes in their large intestine! People with autism nave different microbiomes, people with MS have different microbiomes, kids who are vaccinated have different microbiomes so watch out! And there's not enough papers to do a review paper for six or seven years, and when that review paper is done only other scientists will read it, because Everyone Knows that vaccines alter your microbiome so stay away from them, and Everyone Knows that men and women are so so different because why else would their large intestines be so different? Look at this paper!
And the psych guys are doing MRIs because that's the Next Big Thing, and any bias culturally relevant enough will get positive papers for it because the 100 labs will check their 100 papers and the 95 that show no difference at 95% confidence are of course never published because there's nothing to say. And the medicine guys are running ten thousand seaweed extract experiments to kill cancer cells, and their confidence intervals do tend to be better, but there is no defense against an industry where the only way to survive is to publish and the only way to get funding is to look into something that the grant committee wants you to find something interesting about, and you'd better find that something interesting even if you kind of have to publish your study before repeating it to check for that confidence interval -- someone else will repeat it, or you will once the paper's out, but you've got to get the paper out. And that paper had better be useful for other scientists to reference, even if you have to change the tone and the implications and choose which results are worth referencing, because getting more references is how you keep your job and how you convince the next grant committee.
Big Bad Government Lies About Covid is a problem, but it's not the problem. A perfect government with a massive science budget does not solve the problem. The system of research is inherently political because it is inherently competitive and because it deals with numbers and statistics that are not intuitive to humans.
#fuck capitalism
Nono this problem also exists outside of capitalism. Any economic system has to distribute resources and any actually useful one has to care if its workers are actually producing something.
Publish or perish is a huge problem. It is rewarding "work", but not the useful work that we want to see. All attempts to make those 95 papers see the light of day have been... less than successful. And the rewards for replicating a paper (or at least as useful, showing that you can't) aren't as large as for producing a piece of slop that shows that there's a correlation between green jelly beans an acne.
In my field (cognitive behavioural psychology, and i guess psychology more generally too) this is known as the "file drawer problem", i.e. the vast majority of non-significant findings are left in some researcher's file drawer never to see the light of day because it's not as publishable as the ones that are significant. It's a key factor in what we call the Replication Crisis, where researchers completely failed to replicate several foundational studies on which like half the field was based. Once the first couple effects couldn't be replicated (social priming, ego depletion, power posing, the marshmallow test, etc.), it caused a massive domino effect that revealed widespread replicability issues across the entire field of psychology. Half the famous pop-psychology stuff that gets passed around is based on studies that can't be replicated (and trust me, people have tried really hard to replicate them, it really is egg on our faces for all the most famous bits of our field turning out to be fake). Effects we all thought were really solid turned out to have been statistical anomalies concealed by the file-drawer problem. Stuff that was published in places like Nature and Science. Prominent researchers were exposed for faking huge amounts of their data. Meta-analysis showed suspicious gaps in the distributions of published p-values, indicating people were lying about marginally-significant results (i.e. if the data gave a p-value between 0.05 and 0.06, people were "rounding down" to 0.05 so it technically met the significance threshold). Evidence emerged of researchers collecting massive amounts of data and arbitrarily including or excluding participants until their data showed significant results, or simply collecting more and more data until they had significant results (p-hacking). Papers got retracted, funding was pulled, people got fired. It was a total fucking mess that almost collapsed the entire field, and it happened because of a couple of factors:
a) you can't "prove" anything from non-significant results (i.e failing to find evidence of a link between purple jelly beans and acne is not the same thing as finding evidence *against* a link between purple jelly beans and acne) and so you can't really reach any definitive and interesting conclusions. If you find significant evidence of something you can make all kinds of interesting claims and discuss how it fits or conflicts with existing literature, and propose ideas to explore how and why it works like that, etc. If your findings are nonsignificant, the best you can do is be like "idk man, it's weird it didn't work though, right? Maybe our methods were flawed, or maybe our participants are weird, or maybe there just is no effect here - I sure as fuck couldn't say!" and keep it pushing (while hoping people continue to pay you to do this).
b) Academic journals gain their prestige/value from how often their articles are cited (with for-profit, pay walled journals this is also their main source of revenue bc people/institutions will pay them for access to the articles others have cited, etc.), and so they are motivated to publish things people will want to cite lots. People typically cite articles in order to reference a specific finding or theory it shows evidence for. Therefore, papers with non-significant findings are unlikely to be cited nearly as often as those with significant findings (on account of how they generally don't prove anything) and are thus less valuable for the journal to publish. This maintains the journal's reputation for being difficult to get published in and also for publishing only the most high-quality, "impactful" work (bc everyone is citing it, so it must be important!), ergo the prestige thing. Open-access journals and those dedicated to tackling the file drawer problem are available but are far less prestigious, owing to the lack of citations they get.
c) Academics are judged in hiring and grant application processes by how many papers they have published, how well-cited their work is (ditto above), *and* how prestigious the journals they've published in are. This is the "publish or perish" problem yarning mentioned. In my field applicants for academic positions etc. are often ranked by a combination of Journal Impact Factor (average number of citations received within a two-year window by articles published by the "best" journal you have also published in) and h-index (highest number, h, for which your top h most-cited papers have at least h citations. E.g. if you have 20 papers, and the top 7 are all cited at least 7 times but the next most-cited is only cited twice, you would have a h-index of 7). As you can maybe see, this makes resisting the file-drawer problem (and, to an extent, the other far more unethical methods I mentioned above) quite difficult on the part of individual academics, who need to prioritise publishing work that will get cited if they want to compete for jobs and grants. As an early-career researcher with relatively few published papers, pushing to get your non-significant results published is practically career suicide in a lot of fields especially if you're not working on something that gets a lot of funding opportunities to begin with. It can sometimes look worse to hiring/grant committees to have 6 papers with no citations than to have no papers at all bc the default assumption is often that this means your work is low-quality in some way (this perception has started to change, thankfully). It can be a bit easier for more established researchers for whom publishing low-impact papers doesn't really matter so much and who are probably not having to scrabble for funding etc. as much anyway, but they are also less motivated to push for publication in general.
There isn't a super easy solution to the problem tbh, and it is only compounded by the pressure Derin mentioned to twist whatever you actually want to study to include whatever the hot new analysis/theory/whatever is favoured by your friendly neighbourhood funding-bodies. If people are paying you to look into The Thing, then you do kind of have to try to get it published if you find it, even if it is a bullshit half-baked addition tacked onto the end of the bit you're actually interested in. And conversely, the people invested enough in The Thing to pay you to study it probably don't particularly care if you publish papers that don't find evidence for it, in fact they would probably rather you didn't. Citations are also an abysmally poor metric of the value of a given piece of work to the field, but we don't really have a better one. It *is* becoming more understood how flawed the system is though, and work on large-scale multi-lab replication studies and meta-analyses is gradually being valued more, which is good to see. More journals are also opting for a pre-publication system, where you submit your introduction, rationale, and planned methodology for approval *before* you collect data and get your results. If your pre-pub is accepted, the journal agrees to publish the work regardless of whether the results themselves are significant, which avoids the file drawer problem while also allowing the journal to maintain certain standards of quality based on the methodology and theoretical foundations. Open access, and especially the proliferation of predatory pay-to-publish journals, has gotten a reputation of letting literally anyone publish whatever they like with very poor peer review processes, which contributes to the lack of prestige associated with publishing outside the more traditional journals. The pre-pub system helps to combat that reputation, which makes it more of an option for early-career researchers. Pre-pubs are also often made public in some capacity, which helps to prevent things like p-hacking and retroactively changing hypotheses to fit findings - a solid improvement in transparency imo. Doesn't solve the problem of people potentially straight-up faking their data, but it reduces the incentive to do that stuff if you know you'll get a publication regardless. Funding bodies are springing up whose whole thing is giving grants for replication work across various fields, with expectations that researchers will publish whatever they find, significant or not. There's not a lot of them so far, but at least there are some ways to fund research that aren't so biased towards reinforcing the file drawer problem for whatever the grant committee's pet theory is. It's still a mess, but recent shifts have given me some hope tbh.
Side note bc i can't help myself: The whole "absence of evidence is not evidence of absence" thing is a deliberate feature of how hypothesis testing works, not a bug. You predict what will happen *if the theory you are testing is true* (e.g., if our hypothesis, H¹, is that purple jelly beans are associated with acne, we would predict that participants who eat purple jelly beans would develop more acne than those who don't), and then you run statistical tests to see whether this hypothesis explains the data you actually collect better than assuming there is no effect (which we call a null hypothesis or H⁰) within some pre-determined confidence interval, which is usually 95% or 99% for my field but the standard for statistical significance varies a lot. All your basic, garden-variety statistical hypothesis tests (t-tests, ANOVAs, correlations, regressions, etc.) have a built-in assumption that there is no effect and then test for evidence to contradict that assumption. Because of this, it's impossible to tell a false negative, or the presence of a conflicting effect in the opposite direction, from the actual absence of an effect based on one of these tests. The idea is that we would rather accept a false-negative and dismiss an effect that does exist (which others will surely find evidence of on replication if it is actually reliable) than risk a false positive where we believe in an effect that doesn't exist. In some cases, we use another H⁰, depending on the specific setup of the experiment, whether it's reasonable to assume *no* baseline effect vs. an established level of effect, etc. but it always reflects some baseline assumption that we are comparing H¹ to. Each time you run these tests you can only check for evidence of one H¹ at a time, so failing to find that evidence doesn't mean H⁰ is true necessarily (it could be any number of other alternate explanations), it just means we haven't found enough evidence to suggest H¹ is sufficiently more likely than H⁰, so we can't be confident H¹ is true. If you are testing multiple different hypotheses, and therefore running the same test multiple times, you are *supposed* to add a statistical correction for multiple-comparisons to avoid the inflating family-wise error rate (there are a couple different methods depending on how conservative you want to be with your confidence intervals etc. I won't get into all of that here unless anyone actually wants to know, lol.) This doesn't always happen, especially if multiple different papers are analysing the same dataset since technically, each person is only doing the test once, even though overall you do get the same issue of family-wise error rates. Meta-analyses, where you look at the papers that have already been published on a given topic and analyse what the cumulative evidence says, typically do correct for multiple comparisons in some way, though in my experience.
Plate LXXXVII. Callymenia cribrosa. Icones of Japanese algae v.2. 1912.
Internet Archive
sketches in my tiny sketchbook
gotta be a term that isn't the dunning Krueger effect but is a kind of similar idea where the more deep scientists get into their topic and the more exhausted they get, the more likely they are to find stupid ways to describe things
this post brought to you by a bunch of coral specialists at the end of a workshop voting to define coral, a complex animal, as "a rock with problems"
y'all mind if a magnapinna squid is low poly ?
Plocamium pacificum by bao_634 (iNaturalist, CC-BY-NC)
Sea emeralds (Chaetomorpha coliformis), a type of green algae, on the coast of Wharekauri, Aotearoa
by Svenja Heesch
Just gliding through 🦦
