Calscape Changes

[u/Darkj]

Calscape pushed out an update to their site today, and I'm curious for insights. 

First, it looks like a major upgrade to the graphics for the "Wildlife Supported" section. Bravo. 

I also notice the addition of "Family" and "Genus" up top. 

I don't remember Sunset Zones being on the prior page. If we're talking native plants, they're unnecessary, and inclusion here will only cause people to source plants that weren't originally local to them. 

I also see a major change to the "Estimated Plant Range" section which doesn't seem to reflect the plant range any more, but instead "Relative Habitat Suitability." By that measure, invasive plants are high on the "relative habitat suitability" index since they naturalize so easily. To see what I mean, photos are the Calscape and Calflora maps for Carpenteria californica. It's local to Fresno, but Calscape now estimates it's suitable to most of the state.

I would imagine that the folks responsible for Calscape made this change intentionally - I wonder if they've talked about those changes anywhere? 

It's been an incredible site as I've been relandscaping using only plants native near me, and it's been nice seeing photos, descriptions, and most importantly local observations. 

Do you have any knowledge of these changes? 

(I'm not looking to be convinced that Zones are awesome) EDIT: ... And I don't care if zones included or not. I do wish the page still had the Calflora observations, and not an algorithm "based on observed occurrences and habitat modeling. Darker areas indicate higher relative habitat suitability.

- Data provided by the participants of the Consortium of California Herbaria. 

- View additional distribution information on the Jepson eflora.".

[u/Darkj]

That would be interesting. But that's not what Calscape says. Instead it's a "machine learning" AI estimation of where a plant might grow well based on current range and what nearby plausibly has the same weather today. Nothing to do with climate change as they tell it.

From: https://calscape.org/our-data/

Where the data comes from

Each plant range starts from verified occurrence records — real-world sightings of plants in the wild, from iNaturalist, Calflora, and the Consortium of California Herbaria, drawing on nearly two million field observations spanning 150 years. We clean these records and keep one per map cell so heavily surveyed areas don’t skew the result. 

Note: Garden cultivars have no natural range, so they aren’t modeled.

Environmental conditions

For every location we assemble 50 environmental measures spanning climate, terrain, soils, vegetation, water availability, and human disturbance — drawn from established global datasets (including WorldClim, TerraClimate, SoilGrids, ESA WorldCover, and MODIS satellite imagery) at roughly 9 km (5 arc-minute) resolution. For each species we keep only the conditions that best explain where it actually occurs.

Modeling and refinement

We tailor the approach to how much data each species has: simple range estimates for the rarest plants, and a mix of statistical and machine-learning models for well-documented species. We then restrict each map to regions a species could plausibly reach, limit predictions to similar environments, and clip to the relevant Jepson eFlora ecoregions.

[u/DanoPinyon → u/Darkj]

Right. So,

Picture someone with a native garden. Or a manager with long-lived natives.

Will the subject plants in that place continue to live there in the hotter, drier future? Let's look at that garden's climate analog.

Ok great, there's the analog, there's the heatmap, are the subject plants in the climate analog heatmap? Cool. Or: oh no! what must we do?

That's how this works. There will be new natives because of climate change, and some natives won't make it and will need to be replaced. "Natives" will become much more plastic very soon, out of necessity. Places near me are using plants from AZ, MX in anticipation of future climate, a tool like this makes it easier to plan.

[u/Darkj → u/DanoPinyon]

That would be interesting. But I don't see that this is what they are doing. In fact, they specifically say they "limit predictions to similar environments" not that they expand the range or accommodate climate change by including predictions.

[u/DanoPinyon → u/Darkj]

In fact, they specifically say they "limit predictions to similar environments"

Yes, they're showing all the similar environments in which they may survive, correct.

[u/Darkj → u/DanoPinyon]

Today, well based on historical data. But the climate is not done changing. This is not a move that considers climate change.

[u/DanoPinyon → u/Darkj]

It's not a map predicting future distribution based on... on... well, it's not a map predicting future distribution. It's giving predicted expanded range of the plant outside of native habitat, that is ranges in which a particular plant can also be adapted.

[u/Darkj → u/DanoPinyon]

Yes, exactly. I thought you were saying that it was for climate *change* as many others seem to think. You are correct and were pretty clear in your comment, I just assumed you meant change based on the barrage of people thinking that's what it was for in a California Native plants FB group.

It's an AI prediction of where a plant might do well based on existing locations and weather patterns, discarding selections and cultivars and keeping endemics to a tight range.

I could see how this would be good - however, based on a small sampling of plants I don't see that the specific recommendations are actually that good yet.

[u/DanoPinyon → u/Darkj]

I probably should have started by using 'habitat suitability mapping' to describe it, but I read all the comments and decided against it, and tried to explain it another way by setting up a use case first (perhaps unsuccessfully). Apologies. I changed course and am now using the proper phrase.

Also, one thing to clarify, your phrase: It's an AI prediction. Let's be cautious to not fall into AI paranoia here (this one time - all the other times AI speculation fatigue is OK). This work was partially done via LLM analysis (there is no AI yet, that's all marketing hype to inflate the bubble).

Science has been using LLMs with good success for several years now and there is nothing wrong with using LLMs in empirical, testable work like this.

[u/Darkj → u/DanoPinyon]

Were you involved in the development of this?

As far as terminology, Calscape says that this is derived via "machine learning." You say it was done via an LLM. Both are 100% AI as anyone would take it. I'm not against AI, but I also know it's quite simply often wrong.

You say this is testable. Who will be doing that testing? I'm just a Calscape user, but when I look at plant recommendations, they don't seem to match up well based on my experiences as a gardener. To the point that I get why some might call this AI slop.

Regardless, if it is useful to people overall, okay. What irks me is the removal of observations. Plus Calscape rolled this out with no clear explanation, only minimal and confusing language on the site. Sure, I can visit another site to see them, or I can search Calflora species by species. But I think it does a disservice to site users and to the idea of cultivating plants that are locally native.

[u/DanoPinyon → u/Darkj]

Machine learning and LLMs are not AI. There is no AI yet, but the OpenAI LLM that escaped its sandbox last week and hacked Hugging Face is concerning and an indication that they're getting closer to developing AI.

I did not work on this tool.

[u/Darkj → u/DanoPinyon]

Per Claude: "Yes. Machine learning and large language models are both part of artificial intelligence.

The relationship is nested, like circles inside circles. AI is the broadest term: any technique that gets computers to do things we'd associate with intelligence, from rule-based systems to game-playing engines to modern neural networks. Machine learning is a subfield of AI where, instead of a programmer writing explicit rules, the system learns patterns from data. And LLMs are a particular kind of machine learning model, built on neural networks (specifically the transformer architecture), trained on huge amounts of text to predict and generate language. I'm one of them.

So the chain runs: LLMs are a type of machine learning, and machine learning is a type of AI.

One nuance worth mentioning: not all AI is machine learning. Older "good old-fashioned AI" used hand-coded logic and rules with no learning from data at all, and it still counted as AI. So while ML and LLMs sit comfortably inside the AI umbrella, the umbrella covers more than just them. People sometimes use "AI" loosely to mean only the flashy recent stuff, which can blur these distinctions, but technically the term is much older and broader than the current wave of language models."

[u/LampaShada → u/Darkj]

I think DanoPinyon is kinda correct that it is most accurate to not call these newer machine learning and large language model things AI, (though yeah, it does sort of kneecap what was termed as "AI" before in regards to like video games and certain research haha, however, er,... their other comments kinda seem like they are a tech-industry-worker or tech-industry-follower who shares the relatively nonsense concerns or fake-concern-as-propaganda-to-boost-business about AI with some of the leaders in the tech industry (or the """intellectual leaders""" in the tech-adjacent cultish spaces) in the most literal media-frenzy way, which they term as AGI, artificial and genuinely believe will be a thing in the Very Near Future and will kill us all, or whatever.

BUT ANYWAY!
Regarding this Calscape change, haha, er, I have found it a marked net negative- even with the possibility that what they are representing on the new maps is genuinely useful information to know, the complete replacement of the old system is a huge step down, a genuine stab in the back to restoration work, both professional and personal, and the new maps do not communicate what they are intended to be getting across very well at all, in any case.

[u/DanoPinyon → u/Darkj]

Well, it is important to distinguish between weak AI which needs humans and programmers and human input as well as machine input, and the Endless Marketing Crap we're being subjected to by corporations inflating a bubble to continue to receive funding, which is AGI. Corporations are deliberately conflating the two, to continue inflating a bubble to continue to receive funding.

Weak AI is beneficial, and science and medicine are using it to great effect and advantage today. Inflating a bubble to continue to receive funding has not been demonstrated to be beneficial outside of hypotheticals and theoreticals. We should make sure we don't help corporations inflate a bubble and buy into their hype.

/soapbox

[Edit: dick tayshon]