Diario de tasty_y

Here are all the good links for identifying molluscs that I know (besides the obvious). I'll collect them here mostly for my own convenience, but also for whoever may need them. Sadly, not all the materials are in English. Feel free to post your favorite links in the comments.

• Swedish website: very slick, a list of Swedish species and distribution maps. Nice pictures, some species have multiple pictures of different morphs, which is especially nice. Somewhat lacking when it comes to bivalves. In Swedish.
• German website: a similarly slick database, everything in 𝔊𝔢𝔯𝔪𝔞𝔫. Species, distribution maps (well hidden) and even identification keys. Especially noteworthy: 𝔇𝔢𝔯 𝔖𝔠𝔥𝔩𝔦𝔢ß𝔪𝔲𝔫𝔡𝔰𝔠𝔥𝔫𝔢𝔠𝔨𝔢𝔟𝔢𝔰𝔱𝔦𝔪𝔪𝔲𝔫𝔤𝔰𝔰𝔠𝔥𝔩ü𝔰𝔰𝔢𝔩 (whatever that is), the Pea Clam section (with pretty pictures!) and the accompanying 𝔈𝔯𝔟𝔰𝔢𝔫𝔪𝔲𝔰𝔠𝔥𝔢𝔩𝔟𝔢𝔰𝔱𝔦𝔪𝔪𝔲𝔫𝔤𝔰𝔰𝔠𝔥𝔩ü𝔰𝔰𝔢𝔩 @amr_mn, may be relevant to your interests.
• Carnegie Museum is the only resource on American snails that I know. Snails of Virginia section is especially good, also has a snails of Pennsylvania section, less complete.
• Molluscs of Belarus (in Russian). Not so great pictures, but lots of info on various species.
• Swiss Database (thankfully in English) - mostly distribution maps. Also pretty slick.
• Ukrainian Gastropods: Gural-Sverlova may have contributed a lot to it? Lots of valuable info, though scattered all over the maze-like web-page.
• British distribution maps.
• French Clausiliids - not a website, but a paper with a key. Includes sub-species, very detailed.
• Nature of Latvia - Latvian species lists and distribution maps. Sad, pitiful.
• Another American page

Splitting clausilia dubia and c. pumila is notoriously hard, and there aren't many good photos on the internets anywhere, especially not of pumila. I'll try to put some helpful info here for my own use and convenience as much as for anybody else reading it.

My own comparison photos:

Photos by Aleksandr Anichtchenko, size not to scale:

Photos from a book by Digna Pilāte, no unambiguous attribution:

My own doodle with a summary, exaggerated:

This looks to be the case where the overall shape matters as well as the opening grooves - pumila seems noticeably longer and club-shaped, this is true.

Didn't find any Rangia cuneata today, but saw plenty of dark red sand on the beach today. It's not a rare, but a temporary phenomena: when the waves come at a certain speed at a certain angle, they drag the sand particles along the bottom, and the more heavy and dense grains are less prone to moving so they accumulate in some places and you get these bruise-colored patches of deposits. It's the same process as the one that was used to extract gold from sand in the past. Garnet happens to be quite dense, so it's one of the minerals that tends to accumulate in the fashion. Here's what it looks like:

I collected some samples and put them under magnification:

Turned out there were lots of grains of all sorts of colors. Pink and orange ones are garnets, I assume, and the occasional green ones - olivine. Colorless grains are ordinary quartz, but what could the really dark ones be? Maybe they came from basalt or something.

Something I think could be improved about the design of INat, as a website.

Problem: what I call "senseless caution", both from the human identifiers and the AI: IDing something as a single-species taxon, instead of that species, for example "genus Elona" instead of "Elona quimperiana", or I think "Class Ginkgoopsida" is a striking example with only Ginkgo biloba in it. There's never a reason to register such IDs and they can always be improved without any loss of accuracy. (Logically this should apply to the single genus families and so on, all single-descendant taxons.)

Solution 1: hardcode AI to straight up never recommend single-species taxons and recommend more precise ones instead. Indeed, many such IDs come from silly automatic recommendations.
(More controversially: to be honest, I'd like to go further and forbid AI to ever suggest "Genus Cornu", "Genus Arianta" and the like. Giant waste of time, that.)

Solution 2: Put an "Improve" button on the ID form (next to "Agree" and "Compare") that will only appear on the senselessly cautious IDs. Pressing it would automatically register an ID of the single species in the taxon that appeared in the original ID. (That is: you press the "Improve" button on a "genus Elona" ID and it automatically adds an "Elona quimperiana" ID.)

Solution 3, low impact: add an icon to the senselessly cautious ID (perhaps a blue exclamation point). It would do nothing at all, except that when you moused over it you'd see a text along the lines of: "This identification could be improved without any loss of accuracy by replacing it with taxon such and such." Purely informative.

Objection: I can't think of any strong objections, but you could say: "what if we replace all "genus Elona" with "Elona quimperiana", and then a new Elona species is described, or a taxanomic change happens. Then a clean-up will be required." I think this is reasonable but in practice much less of an annoyance than the one that senseless caution currently creates.

(Not posting this on the forum)

Today I met Malacologist G. and got to see her private collection. (I'll need to be slightly cryptic about this for privacy reasons.)

It was a lot of fun! If I had the opportunity, I could spent the entire day looking at each shell individually, one by one. I got to see the cool local species I haven't found yet and give G. my Physa acuta sample. Got to ask the questions I always wanted to. I should write down all that I learned while it's still fresh in my memory:

1) Q: Is there really Alinda biplicata in Latvia? A: Nah.

2) Q: Is there really Ferrissia californica? A: probably not really, it can't stand sub-zero temperatures, maybe.

3) Q: What's the diference between perpolitas? A: I still can't make much sense of it, will need to look at a lot more pictures.

4) Q: Where can I find Cochlicopa lubricella? A: Check the dry places. Also, it's supposed to be pale when alive?

5) Q: Is there really Monachoides incarnatus in Latvia? A: Probably not really. Didn't even have a sample.

6) Q: Is there Oxyloma sarsii in Latvia? A: Somebody may have found it in a greenhouse.
6.1) Q: But could it be like, totally abundant all over the place and it's just that nobody even bothered to dissect and find out? A: May be!

7) Q: will it be possible to donate my collection to the museum when I'm old and frail? A: No promises or guarantees. Not even with data. (This is depressing. I may just be forced to sell all of it.)

8) Q: Why isn't all the Latvian data on Gbif yet? A: They are working on it. Something-something bureaucracy. Something-something need to hire programmers. (This is depressing. It's overwhelmingly important to get all that on Gbif. Thank goodness my data is in there.)

Never even got to ask about Gastrodontoids.

• Supposedly there are some Hydrobia species in Latvia that there were no samples of and that would be good to find.
• G. is in the camp "no, you can't tell Ambersnails apart based on the shells at all, not even s. putris vs genus oxyloma, no, don't even try, it's hopeless". It's true that God created Theodoxus as a gift to malacologists, and Amebersnails - as a punishment for their hubris. Also, apparently you need to prepare them in some weird way before dissecting them, ie. not in alcohol.
• G. would be interested in Rangia cuneata samples. Oh, I better find some!
• G. is totally aware that Cochlicopa nitens tends to have an S-shaped columella. That's right, Bernhard! It does! It does have it!
• The reason I never found gyraulus crista is that crysta is way, way tiny and fragile. I probably missed it a hundred times. Will need to level my dirt-digging skill for this.

We commiserated in that:

• Pusidium is unbearable pain
• Gyraulus is somewhat bearable pain.
• Radix is sadface.

Once upon a time many years ago, I was in a financially unpleasant situation and I hatched a plan: I would paint and sell some pictures (of seashells, because of course). That plan never got even as far as offering anything to anybody, but I did paint some pictures. Shells are fun to paint, and you also get to feel like Ernst Haeckel or some other scholar-illustrator of old.

Can you ID all the shells?

(I don't intend to sell the pictures anylonger, don't get the idea that this is an ad.)

There's a particular conversation that plays out on the internet about 100 every day. It goes like this:

Person 1: I've noticed that something weird is going on. I have this data, and it's not the way you would normally expect it to be. Strange!

Person 2: Well, how many data points have you got?

Person 1: 20.

Person 2: Ah, well you see this is a very small sample size. We can't draw any conclusions from your data, because there's just too little of it, so more data is needed and we have no reason to think anything strange could be going on just yet.

Person 1: I see! You are very wise: 20 is a very small sample size indeed.

What do you think about this conversation? Is Person 2 very wise and prudent to see number 20 and say it's too small do draw conclusions from?

This conversation plays out over and over, countless times. Person 2 never bothers to say what sample size would be big enough for their liking, never bothers to specify what sort of model they are working under, never bothers to look at the actual data Person 1 is providing and crunch the numbers to find out the p-value. All they knows is that no matter how many data points are presented, you can always look at them and sagely declare that it's too few and there's no reason to suspect anything odd. 20? Too small. 200? Too small. 20000? Still too small.

How big of a sample size do you actually need to tell anything? Turns out, you can't answer that question in vacuum. It completely depends on your assumptions (H0) and on what sort of data you got.

Imagine that you think that the probability to look outside your window and see a polar bear is 1 in a billion. That's your null hypothesis. You conduct one single experiment: you look outside and behold - a polar bear waving its paw at you. What do you say now, do you say: "well, 1 is a very small sample size, nothing strange is going on here, no reason to suspect my assumption was stupid"? No. You say: "under my null hypothesis there's 1 in a billion chance to produce the result I obtained. So the hypothesis can be safely rejected with a high level of confidence. (p=10^-9)". You can perform more experiments, but you don't need to: with this null hypothesis and this data, 1 is a completely sufficient sample size.

Let's look at another example. Somebody hands you a die and tells you it's fair, that is equally likely to land on any number. You roll the die 5 times, and every time it produces 1. So, do you have any reason to think there might be something fishy with it, or must you say that 5 is a small sample size and it doesn't mean anything? Well, do the arithmetic. Under that hypothesis that the die is fair, the probability that it will produce data this skewed is 1/6^4 = 1/216 = 0.004. Certainly you can say that it's still reasonably high, certainly you can roll the die a couple more times if you wish to be more sure. But notice how with the sample size of 5 we can already reject the initial assumption with a 0.4% confidence. This is better that what lots of published papers can claim. Turns out, with this data 5 is a pretty healthy sample size! (It won't always be so: if you rolled the die 10 times and the results were more mixed, perhaps 10 wouldn't be enough).

The silliest real-life example of this that I've seen was when one person recorded the outcomes of the random effect in a video game. They seems weird and unfair to him, and he recorded about 150 of them. He finished the post by declaring that of course it doesn't mean much, after all 150 is a very small sample size. When we worked through the numbers, it turned out that his data (or data at least that weird) had a 1/50000 probability of occurring under the assumption that the game was fair. His sample was more than big enough, and still he insisted that it was probably too small (without crunching any numbers, of course).

Moral of the story: there's no such thing as "small sample size" in isolation from the data and the null hypothesis. Don't go around confidently declaring that some number less than a billion is a "small sample size" without doing the hard work of calculating what number would be sufficient, without looking at the data and formulating your null hypothesis. We learn statistics so that we can notice that something is weird when there is reason to, not so we can dismiss literally anything shown to us as a coincidence and "small sample size".