From rabbits and foxes to the human gut microbiome, physics is helping us understand the natural world

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Hello, and welcome to the Physics World Weekly

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podcast.

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I'm Margaret Harris, and this episode of the

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podcast is sponsored by EPL,

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a journal that publishes original, high quality letters

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in all areas of physics.

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EPL operates under the scientific policy and control

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of the European Physical Society

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and is published by EDP Sciences,

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the Societa Italiana de Physica,

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and IOP Publishing, which also publishes Physics World.

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My guests in this episode are the co

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authors of an invited mini review article or

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perspective

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in EPL on the subject of theoretical ecology.

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The article was published in June, its title

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is

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Statistical Physics Approaches to Ecological Communities,

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and its authors are Ada Altieri and Silvia

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deMonte.

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Ada is an associate professor at the Laboratory

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for Matter and Complex Systems at the Universite

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Paris Cite, France,

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and Sylvia is a senior researcher at the

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Institute of Biology in the Ecole Normale Superieure

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in Paris and the Max Planck Institute for

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Evolutionary Biology in Pleuron, Germany.

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Ada Altieri and Silvia de Monte, welcome to

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the podcast.

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Thank you. Thank you so much. Okay. The

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first thing I want to get straight for

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our listeners is

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what is theoretical ecology? You know, what kinds

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of questions are you asking? What kinds of

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problems are you trying to solve?

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Perhaps, I will start.

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So theoretical ecology is a pretty broad field

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that has a long history.

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It has,

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traditionally, we think, it started

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around the beginning of the 20 century with,

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the proposal of using some low dimensional models,

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so models of interaction between, just a few

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species, typically two species,

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to try and understand

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some patterns that were observed

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in,

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ecological systems.

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So the sort of the chief,

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model people refer to is, the Lotka Volterra

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predator prey equations

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that were

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independently

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proposed,

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by

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Lotka in,

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1910

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and by Volterra

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in 1926

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to explain,

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why

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natural populations seem to vary

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over, different years in the number of individuals

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in ways that

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looked a bit

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disconnected,

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from, the environmental

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year to year variations.

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And so they realized,

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and this was a really an important realization

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that ecological systems

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might be maintained out of equilibrium

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by the interactions

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between species.

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So if you have just, you know, a

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few species and you're able to know what

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is, the probability of interaction or what are

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the growth rates of one and the other,

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you can still rely on this kind of

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models. But there is another

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vast array of models that have been developed,

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especially in the last thirty years or so

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that deal with,

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communities that are composed of a vast number

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of species

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where it is

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much more difficult to estimate parameters.

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I want to bring Ada in just because

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I want to get a couple of examples

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if I can of the types of species

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that you're talking about in communities. You know,

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what's what was a good example of a

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two species interaction that's very easy to model

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with these

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equations that were developed in 1910, 1926?

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We can think of just prey predator,

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so fox and rabbit,

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kind of species.

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But as Heather was saying, so the most

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remarkable

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aspect of recent years, so the true novelty

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right now is that we are trying

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to characterize emergent mechanisms and collective behaviors of

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large ecological communities,

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for instance, gut microbial communities in terms of

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odor versus disorder phase transition.

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So this is one of the goal of

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of our approach.

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And the second one is also to to

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provide

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quantitative

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estimators, so quantitative discriminators

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for detecting

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different rigids

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in this kind of communities.

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What kind of extra complexities does it add

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trying to model, say, microbial species? I think,

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Sylvia, you were talking about sometimes

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you can't get accurate estimates in the way

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you can get accurate estimates of, say, foxes

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and rabbits.

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Yeah. Indeed. So these microbial communities, they really

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have a huge number of species

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in

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many environments, natural environments.

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And you can sometimes reconstruct synthetic communities in

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the lab. And then these synthetic communities allow

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you to measure growth rates, to measure interaction

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rates.

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But this involves

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estimating

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a large number of parameters and a lot

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of experimental work.

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This can be done for relatively

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simple communities

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and, in control settings.

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However,

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when you deal with communities,

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in nature or, in a gut,

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you can't really go and extract one species

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and or measure in situ.

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So

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you often

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only have an idea

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of what kind

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of statistical

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features

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have the distribution

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of interactions.

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Or at least this is what our work

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is about,

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trying to infer, for instance, the statistical features

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from something that can

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be measured

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at the level of the community

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without knowing

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exactly

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what's in every single species is doing.

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So can you use statistical physics models to

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predict what will happen

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in real ecosystems,

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like, I don't know, tipping points or circumstances

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which

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one species in a community might become extinct

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or the community itself becomes unstable?

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Yeah. So for instance, a small change in

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environmental

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condition or system parameters

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that can be demographic

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fluctuations

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of birth and the processes

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can cause a sudden turnover,

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so irreversible

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shift

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from one equilibrium state to another equilibrium state.

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So we can, for instance, detect a phase,

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a self sustaining

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phase where all species

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survive

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and another phase where all species get extinct.

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So this kind of transition

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is typically referred to as a tipping point

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and in theoretical and statistical physics

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is also called

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a spinodal

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transition

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because it's associated with a vanishing

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eigenvalue

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of a stability matrix a stability matrix,

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which provide information on the emergent criticality, the

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emergent transition.

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So following intuition from statistical physics, we can

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try

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to estimate and characterize these tipping points.

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Empirically

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speaking, in real ecosystem,

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one could measure fluctuation

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of the average abundance

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with respect to environmental noise, which affect the

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current capacity, so the maximum attainable value of

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a species abundance,

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or also individual

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growth rates.

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So this is perfectly

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doable in in a real ecosystem

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and provides

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early word signal

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of the proximity

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to a deep endpoint.

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How applicable is your work to actually colleges

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out in the field? Is this one of

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your goals, or is it all very far

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removed several steps back from that?

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So I suppose that in the field, there

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are people who,

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do this.

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So

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I personally

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don't, go that far because,

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my

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focus

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and that of my collaborators

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is mostly

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to try and understand

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how

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the mechanisms,

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that are involved in ecology

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shape

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patterns that can be observed at the level

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of a community.

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And the reason we focus on this is

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that it is still pretty poorly understood

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how

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processes

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turn into patterns.

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So what we can observe typically

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are patterns.

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Like, I don't know, we we go out

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in the field

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and we measure how many individuals are there

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of every species.

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So if it's a microbial community, there are

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many, many different species, hundreds or thousands of

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different species. And then there are ways to

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estimate

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how many

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of each of these species is there.

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Once you have this kind of information,

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you can

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write down histograms,

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do some

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statistics

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on this data.

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And these statistics, they turn out to be

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sometimes,

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different from one ecosystem to another, but sometimes

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they are incredibly similar.

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And we still

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don't understand completely

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why certain patterns

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are so

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conserved or universal if you want, to use,

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a physics term.

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And sometimes instead, we would like actually to

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focus on differences rather than on what is

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the same everywhere.

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So

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our,

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goal is mainly to

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understand

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what determines

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this,

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regularities,

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and what determines the differences.

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And then, of course, the the ultimate goal

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would be to be able to go out

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in nature and to be able to say,

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okay, this ecosystem that has these statistical features

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is particularly endangered, for instance,

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rather than this other one is instead,

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you know, a stable ecosystem.

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We observe, for instance, this kind of differences

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between, ecosystems

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that are,

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like,

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associated,

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to hosts. So they have evolved for a

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long time

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within a host. So they are sort of

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more,

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stable in in their functioning

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rather than ecosystems,

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like, I don't know, plankton ecosystems,

294
00:10:41,004 --> 00:10:42,865
that have a very, very high turnover,

295
00:10:43,669 --> 00:10:46,389
and, they tend to have many species that

296
00:10:46,389 --> 00:10:49,610
are rare and plausibly to an extinction threshold.

297
00:10:50,389 --> 00:10:52,549
I like the distinction you made between patterns

298
00:10:52,549 --> 00:10:54,230
and processes. You want to be able to

299
00:10:54,230 --> 00:10:56,169
go out and see a pattern in nature

300
00:10:56,230 --> 00:10:58,329
or in the gut or in the laboratory

301
00:10:58,804 --> 00:11:01,284
and say, ah, because I see this pattern,

302
00:11:01,284 --> 00:11:02,964
these are the processes that are going on,

303
00:11:02,964 --> 00:11:03,625
and therefore,

304
00:11:04,084 --> 00:11:06,804
this is what might happen next. Yep. That's

305
00:11:06,804 --> 00:11:09,284
exactly what people have been trying to do

306
00:11:09,284 --> 00:11:10,264
for a long time.

307
00:11:10,899 --> 00:11:13,220
The fact is that, so as we review

308
00:11:13,220 --> 00:11:14,039
in the article,

309
00:11:14,500 --> 00:11:17,940
there had been many different approaches where people

310
00:11:17,940 --> 00:11:20,679
started from hypothesis that seem to be ecologically

311
00:11:20,820 --> 00:11:21,320
reasonable,

312
00:11:21,860 --> 00:11:24,175
and then they'd use the pattern. And then

313
00:11:24,175 --> 00:11:26,095
they went out in nature, looked at the

314
00:11:26,095 --> 00:11:28,654
pattern that looked like that and said, ah,

315
00:11:28,654 --> 00:11:31,134
okay. I I believe then then my model

316
00:11:31,134 --> 00:11:31,955
is right.

317
00:11:32,654 --> 00:11:33,154
But

318
00:11:33,535 --> 00:11:34,754
one of the problems

319
00:11:35,055 --> 00:11:38,129
that has emerged is that different models tend

320
00:11:38,129 --> 00:11:40,149
to provide very similar patterns

321
00:11:40,529 --> 00:11:42,149
that are very hard to distinguish

322
00:11:42,769 --> 00:11:44,230
from empirical data.

323
00:11:44,690 --> 00:11:45,350
And then

324
00:11:45,730 --> 00:11:46,529
it is still,

325
00:11:47,330 --> 00:11:49,590
one of the open questions that we raised,

326
00:11:50,529 --> 00:11:53,424
in the more perspective part. So what is

327
00:11:53,424 --> 00:11:53,924
exactly

328
00:11:55,184 --> 00:11:56,404
that is peculiar

329
00:11:56,784 --> 00:11:58,084
of a given model

330
00:11:58,464 --> 00:12:01,825
and, how can we distinguish models that start

331
00:12:01,825 --> 00:12:03,365
from very different assumptions

332
00:12:03,985 --> 00:12:05,125
Mhmm. Based on data?

333
00:12:05,929 --> 00:12:07,929
Okay. So as you've already alluded, you've written

334
00:12:07,929 --> 00:12:10,250
the paper recently for the journal EPL in

335
00:12:10,250 --> 00:12:13,549
which you discussed some basic classes of statistical

336
00:12:13,690 --> 00:12:17,949
physics models that you as theoretical ecologists use

337
00:12:18,409 --> 00:12:20,409
and the challenges that future models need to

338
00:12:20,409 --> 00:12:20,909
address.

339
00:12:21,235 --> 00:12:22,754
I wonder if we could discuss a few

340
00:12:22,754 --> 00:12:23,735
of these challenges

341
00:12:24,274 --> 00:12:26,355
in a bit more depth. We've already talked

342
00:12:26,355 --> 00:12:30,195
about species interactions, including higher order interactions. What

343
00:12:30,195 --> 00:12:30,934
does it mean?

344
00:12:31,475 --> 00:12:34,434
So it basically means that the interaction between

345
00:12:34,434 --> 00:12:35,159
two species

346
00:12:35,559 --> 00:12:38,120
is modulated by the presence of a third

347
00:12:38,120 --> 00:12:40,139
one or a fourth one.

348
00:12:40,440 --> 00:12:42,059
So suppose that species

349
00:12:42,519 --> 00:12:43,339
a competes

350
00:12:43,879 --> 00:12:45,819
with species b for nutrients,

351
00:12:46,360 --> 00:12:48,600
then suppose now that the strength of this

352
00:12:48,600 --> 00:12:49,100
competitive

353
00:12:49,480 --> 00:12:50,860
effect is reduced

354
00:12:51,445 --> 00:12:54,264
if species c is present because

355
00:12:55,284 --> 00:12:57,784
c improves soil condition via

356
00:12:58,164 --> 00:12:59,384
nitrogen fixation

357
00:12:59,845 --> 00:13:01,065
or other effect.

358
00:13:01,445 --> 00:13:04,345
So overall, a, b, and c work

359
00:13:04,804 --> 00:13:05,464
out altogether

360
00:13:06,129 --> 00:13:09,029
and affect both the stability and the dynamics

361
00:13:09,409 --> 00:13:10,309
of the community.

362
00:13:10,850 --> 00:13:12,789
So on one hand, I higher interaction

363
00:13:13,250 --> 00:13:16,769
have the potential to add another layer of

364
00:13:16,769 --> 00:13:17,269
complexity,

365
00:13:17,809 --> 00:13:18,949
to add more biological

366
00:13:19,409 --> 00:13:22,695
complexity and realism to the community. But on

367
00:13:22,695 --> 00:13:25,195
the other hand, they further complicate the analysis.

368
00:13:25,894 --> 00:13:29,735
So rigorously understanding how relevant they are in

369
00:13:29,735 --> 00:13:33,014
maintaining diversity in the community is still an

370
00:13:33,014 --> 00:13:33,835
open question.

371
00:13:34,375 --> 00:13:35,835
I would just perhaps

372
00:13:36,440 --> 00:13:37,820
step back a second

373
00:13:38,360 --> 00:13:40,059
and just say that,

374
00:13:40,679 --> 00:13:41,179
many

375
00:13:41,879 --> 00:13:43,500
simplified models of ecosystems

376
00:13:44,200 --> 00:13:46,700
usually only consider pairwise interactions.

377
00:13:47,559 --> 00:13:50,215
It is certainly more realistic to take into

378
00:13:50,215 --> 00:13:52,075
account higher order interactions

379
00:13:52,855 --> 00:13:55,975
even though most results have been obtained so

380
00:13:55,975 --> 00:13:59,595
far for this simple setting where basically

381
00:14:00,134 --> 00:14:01,355
interactions among

382
00:14:01,839 --> 00:14:03,299
a very complex community

383
00:14:03,679 --> 00:14:06,559
just boil down to interaction between pairs of

384
00:14:06,559 --> 00:14:07,059
species.

385
00:14:08,000 --> 00:14:08,980
Another simplification

386
00:14:09,600 --> 00:14:13,139
is this idea that obviously any real ecosystem,

387
00:14:13,360 --> 00:14:16,019
even a bacterial ecosystem with millions of individuals

388
00:14:16,080 --> 00:14:17,625
will have a finite number of species.

389
00:14:18,745 --> 00:14:20,924
Why is this hard to model mathematically?

390
00:14:21,705 --> 00:14:24,985
Indeed, ecological communities have a varying number of

391
00:14:24,985 --> 00:14:25,485
species.

392
00:14:26,184 --> 00:14:27,404
So macroorganisms

393
00:14:28,345 --> 00:14:28,825
like,

394
00:14:29,225 --> 00:14:30,445
animals and plants

395
00:14:31,470 --> 00:14:34,690
have communities that are composed of, you know,

396
00:14:35,230 --> 00:14:38,129
at maximum a few 100 of different species.

397
00:14:38,830 --> 00:14:42,049
And this is the number is much higher

398
00:14:42,110 --> 00:14:42,610
for

399
00:14:43,245 --> 00:14:44,304
microbial communities.

400
00:14:45,085 --> 00:14:47,424
But, indeed it is not infinite.

401
00:14:48,205 --> 00:14:50,705
So the problem is to find

402
00:14:51,164 --> 00:14:52,945
the right spot between,

403
00:14:53,725 --> 00:14:56,764
very low dimensional systems. So that can be

404
00:14:56,764 --> 00:14:57,264
analyzed

405
00:14:57,830 --> 00:14:58,490
with methods,

406
00:14:58,950 --> 00:15:01,290
from, I don't know, bifurcation analysis,

407
00:15:01,830 --> 00:15:03,290
dynamic system theory,

408
00:15:03,830 --> 00:15:06,410
and these other models that come from statistical

409
00:15:06,470 --> 00:15:08,730
physics, where you have in principle,

410
00:15:09,029 --> 00:15:10,649
an infinite number of species.

411
00:15:11,654 --> 00:15:14,715
What turns out is that in many cases,

412
00:15:15,254 --> 00:15:18,154
even though, results are obtained in the thermodynamic

413
00:15:18,455 --> 00:15:18,955
limit,

414
00:15:19,335 --> 00:15:21,995
a lot of conclusions still hold for

415
00:15:22,535 --> 00:15:24,394
a reasonable number of species.

416
00:15:24,860 --> 00:15:27,339
So, at a certain point, of course, this

417
00:15:27,339 --> 00:15:28,159
breaks down,

418
00:15:28,620 --> 00:15:31,500
but there are even some features that are

419
00:15:31,500 --> 00:15:32,000
actually

420
00:15:32,620 --> 00:15:33,120
strictly

421
00:15:33,820 --> 00:15:34,639
low dimensional

422
00:15:35,019 --> 00:15:37,519
or, low co dimensional, and therefore

423
00:15:37,820 --> 00:15:38,720
they can be,

424
00:15:39,384 --> 00:15:39,884
described

425
00:15:40,264 --> 00:15:42,345
even, with, tools from,

426
00:15:42,904 --> 00:15:44,365
dynamical system theory

427
00:15:44,825 --> 00:15:47,404
despite, being emergent from

428
00:15:47,705 --> 00:15:49,625
a system with a very, very high number

429
00:15:49,625 --> 00:15:50,764
of degrees of freedom.

430
00:15:51,705 --> 00:15:54,360
I will also like to say that the

431
00:15:54,360 --> 00:15:56,759
kind of techniques that we are describing in

432
00:15:56,759 --> 00:15:59,559
our review are typically referred to as mean

433
00:15:59,559 --> 00:16:02,460
field approach. So one of these is dynamical

434
00:16:02,519 --> 00:16:05,659
mean field theory. And so it's very helpful.

435
00:16:05,960 --> 00:16:08,554
It's very useful to characterize

436
00:16:09,095 --> 00:16:11,195
emergent mechanisms, collective behaviors,

437
00:16:11,735 --> 00:16:14,934
but it has been also shown that it

438
00:16:14,934 --> 00:16:17,914
can fail in the presence of sparse networks.

439
00:16:18,470 --> 00:16:21,429
So when the network is not completely connect

440
00:16:21,429 --> 00:16:23,290
and each node, each species

441
00:16:23,830 --> 00:16:27,429
is just connected, is just related to a

442
00:16:27,429 --> 00:16:27,929
few

443
00:16:28,230 --> 00:16:28,889
of them.

444
00:16:29,269 --> 00:16:31,769
And so in this case, we should improve

445
00:16:32,195 --> 00:16:32,855
and refine

446
00:16:33,154 --> 00:16:36,195
our techniques to take into account also finite

447
00:16:36,195 --> 00:16:36,934
size fluctuations.

448
00:16:37,554 --> 00:16:39,394
And so combine it at the same time

449
00:16:39,394 --> 00:16:40,534
numerical simulation

450
00:16:40,835 --> 00:16:43,254
with rigorous analytical method.

451
00:16:44,309 --> 00:16:47,029
What does failure look like when you're modeling

452
00:16:47,029 --> 00:16:48,629
a system like this? How do you know

453
00:16:48,629 --> 00:16:50,070
when your model has failed? Do you have

454
00:16:50,070 --> 00:16:52,149
to compare it with experimental data or you're

455
00:16:52,149 --> 00:16:54,230
just like, oh gosh. That's pretty something really

456
00:16:54,230 --> 00:16:54,730
unphysical?

457
00:16:55,830 --> 00:16:57,125
Yeah. So experimental

458
00:16:57,504 --> 00:17:00,245
data that are available or rather,

459
00:17:00,545 --> 00:17:02,165
let's say, empirical data

460
00:17:02,625 --> 00:17:03,125
for,

461
00:17:04,065 --> 00:17:05,125
natural communities

462
00:17:05,664 --> 00:17:07,025
are typically this,

463
00:17:07,424 --> 00:17:09,605
measures of species abundances.

464
00:17:10,470 --> 00:17:12,890
So this can be either like a snapshot

465
00:17:13,269 --> 00:17:13,769
measures.

466
00:17:14,309 --> 00:17:15,929
Like, I go out in the ocean,

467
00:17:16,390 --> 00:17:17,130
I sample,

468
00:17:17,669 --> 00:17:19,690
I come back, and then I sequence,

469
00:17:20,230 --> 00:17:21,929
whatever I find in the water.

470
00:17:22,789 --> 00:17:25,190
Or in some cases and actually more and

471
00:17:25,190 --> 00:17:25,929
more luckily,

472
00:17:26,284 --> 00:17:29,244
there are time series. So there are, you

473
00:17:29,244 --> 00:17:31,984
know, one can access the variation over time

474
00:17:32,204 --> 00:17:33,984
of the abundance of different species.

475
00:17:34,605 --> 00:17:37,904
And so this time series are particularly valuable

476
00:17:38,284 --> 00:17:38,784
because,

477
00:17:39,164 --> 00:17:41,099
they provide a different perspective

478
00:17:55,099 --> 00:17:56,079
abandoned again.

479
00:17:56,865 --> 00:17:59,924
So if we don't access this temporal variation,

480
00:18:00,384 --> 00:18:01,845
we will never know exactly

481
00:18:02,384 --> 00:18:03,204
which species

482
00:18:03,825 --> 00:18:04,565
are abundant,

483
00:18:05,505 --> 00:18:06,565
and whether

484
00:18:07,105 --> 00:18:08,884
there is something that is interesting

485
00:18:09,559 --> 00:18:12,059
also in the dynamics and not only

486
00:18:12,440 --> 00:18:14,460
on, you know, our snapshot

487
00:18:14,759 --> 00:18:16,299
vision of the ecosystem.

488
00:18:17,240 --> 00:18:20,359
So this goes back to the idea by

489
00:18:20,359 --> 00:18:23,615
Lotka and Volterra that, you know, ecosystems need

490
00:18:23,615 --> 00:18:24,755
not be at equilibrium.

491
00:18:25,294 --> 00:18:27,474
They can also live, in states,

492
00:18:27,775 --> 00:18:28,994
that are variable.

493
00:18:29,454 --> 00:18:32,255
And we know now, especially thanks to the

494
00:18:32,255 --> 00:18:33,394
latest developments,

495
00:18:34,190 --> 00:18:37,549
where statistical physics has contributed a lot, that

496
00:18:37,549 --> 00:18:39,329
there are many different,

497
00:18:40,029 --> 00:18:41,329
out of equilibrium regimes,

498
00:18:41,710 --> 00:18:42,849
that can be partially

499
00:18:43,470 --> 00:18:45,730
described by statistical physics methods.

500
00:18:46,625 --> 00:18:47,525
Do you have an example

501
00:18:47,825 --> 00:18:50,305
of something that statistical physics has been very

502
00:18:50,305 --> 00:18:52,325
successful in describing and out of the equilibrium

503
00:18:52,384 --> 00:18:52,884
system?

504
00:18:53,825 --> 00:18:55,765
So something we have been studying

505
00:18:56,305 --> 00:19:00,005
are the distribution of abundances of planktonic

506
00:19:00,384 --> 00:19:01,365
protest communities.

507
00:19:01,940 --> 00:19:02,819
So protests are,

508
00:19:03,619 --> 00:19:04,759
unicellular eukaryotes.

509
00:19:05,700 --> 00:19:07,319
And, there have been surveys,

510
00:19:07,700 --> 00:19:09,160
in the last years,

511
00:19:09,859 --> 00:19:10,339
where,

512
00:19:10,980 --> 00:19:12,359
basically the whole

513
00:19:13,059 --> 00:19:14,119
the total biodiversity

514
00:19:14,740 --> 00:19:17,365
of the sunlit ocean has been revealed

515
00:19:17,825 --> 00:19:18,644
by sampling

516
00:19:19,345 --> 00:19:21,845
in many different locations in the world ocean.

517
00:19:22,384 --> 00:19:24,964
And one striking feature there was,

518
00:19:25,345 --> 00:19:25,845
that

519
00:19:26,625 --> 00:19:27,524
most species

520
00:19:27,984 --> 00:19:29,365
appear to be rare,

521
00:19:30,289 --> 00:19:32,069
and one could assess

522
00:19:32,450 --> 00:19:35,509
what was the law of decay of abundances.

523
00:19:36,690 --> 00:19:39,329
So if you rank species from the most

524
00:19:39,329 --> 00:19:40,869
abundant to the least abundant.

525
00:19:42,369 --> 00:19:43,190
And methods

526
00:19:45,065 --> 00:19:47,725
derived by statistical physics and

527
00:19:48,505 --> 00:19:50,525
using this, lot of models,

528
00:19:51,144 --> 00:19:53,625
this time a generalized lot of models. So

529
00:19:53,625 --> 00:19:54,125
with

530
00:19:54,585 --> 00:19:56,125
many, many different species

531
00:19:56,700 --> 00:19:57,440
allowed to

532
00:19:57,980 --> 00:19:59,359
reproduce, the observation,

533
00:20:00,619 --> 00:20:01,919
that this decay,

534
00:20:02,539 --> 00:20:04,880
of the so called species abundance distribution

535
00:20:05,819 --> 00:20:08,460
is actually a power law. And that this

536
00:20:08,460 --> 00:20:11,015
power law has an exponent that is different

537
00:20:11,015 --> 00:20:13,654
from one that is, the typical exponent of

538
00:20:13,654 --> 00:20:14,315
the Ziff's

539
00:20:14,775 --> 00:20:15,994
law. Models also

540
00:20:16,615 --> 00:20:17,414
sort of,

541
00:20:17,734 --> 00:20:21,015
retrieve exponents that are in the same order

542
00:20:21,015 --> 00:20:22,394
of magnitude. So,

543
00:20:22,934 --> 00:20:25,380
that is between, let's say, one and two

544
00:20:25,700 --> 00:20:27,720
as those that are observed empirically.

545
00:20:28,579 --> 00:20:30,900
And this, and this kind of observation is

546
00:20:30,900 --> 00:20:31,400
actually

547
00:20:31,940 --> 00:20:33,640
linked in, with

548
00:20:34,019 --> 00:20:35,799
different models. So some of them,

549
00:20:36,180 --> 00:20:36,680
so

550
00:20:37,140 --> 00:20:40,599
this lots of Volterra, some others, neutral models.

551
00:20:41,434 --> 00:20:43,454
But lots of Volterra models are particularly

552
00:20:43,835 --> 00:20:46,734
useful because they also provide a dynamical

553
00:20:47,275 --> 00:20:50,234
picture of, what is the underpinning of this

554
00:20:50,234 --> 00:20:50,734
distribution.

555
00:20:51,755 --> 00:20:54,634
That is, the idea that there are at

556
00:20:54,634 --> 00:20:55,694
a given time,

557
00:20:56,130 --> 00:20:58,450
a few species that are highly dominant and

558
00:20:58,450 --> 00:20:58,950
successful,

559
00:20:59,490 --> 00:21:02,450
but this highly successful species do not stay

560
00:21:02,450 --> 00:21:05,109
there forever, and there is a permanent turnover

561
00:21:05,890 --> 00:21:08,150
of successful and rare species.

562
00:21:08,764 --> 00:21:10,764
So all the diversity that is,

563
00:21:11,244 --> 00:21:14,384
observed is actually necessary to maintain

564
00:21:15,404 --> 00:21:15,904
overall

565
00:21:16,444 --> 00:21:17,904
this very high diversity

566
00:21:18,605 --> 00:21:19,984
of controlling communities.

567
00:21:21,019 --> 00:21:23,419
Which presumably has some implications for conservation. You

568
00:21:23,419 --> 00:21:25,339
can't just go out and save one species

569
00:21:25,339 --> 00:21:26,779
because you have to save them all if

570
00:21:26,779 --> 00:21:29,419
you want to preserve the Absolutely. Absolutely. Exactly.

571
00:21:29,419 --> 00:21:32,000
Because if you throw away the rare species,

572
00:21:32,220 --> 00:21:34,399
actually, your system stops oscillating

573
00:21:34,940 --> 00:21:35,440
and

574
00:21:36,214 --> 00:21:37,674
stops this dynamics

575
00:21:38,695 --> 00:21:39,674
of turnover.

576
00:21:41,015 --> 00:21:42,855
I want to get into this idea of

577
00:21:42,855 --> 00:21:45,414
the importance of time in in the modeling

578
00:21:45,414 --> 00:21:47,174
because one of the things you say in

579
00:21:47,174 --> 00:21:49,255
your paper is is actually quite hard to

580
00:21:49,255 --> 00:21:53,230
model is evolution. Now all organisms evolve, and

581
00:21:53,230 --> 00:21:55,950
some organisms evolve fairly slowly because they only

582
00:21:55,950 --> 00:21:56,450
have

583
00:21:56,909 --> 00:21:59,630
descendants every few years. But bacteria, of course,

584
00:21:59,630 --> 00:22:02,029
which you're talking about modeling a lot, they

585
00:22:02,029 --> 00:22:04,215
evolve quite quickly. Why is this hard to

586
00:22:04,215 --> 00:22:05,974
model, and why is it so important that

587
00:22:05,974 --> 00:22:07,835
future models should capture it?

588
00:22:09,174 --> 00:22:12,875
Yeah. Evolution is is really the central feature

589
00:22:13,335 --> 00:22:13,815
of,

590
00:22:14,295 --> 00:22:15,035
of life.

591
00:22:15,414 --> 00:22:17,674
And, indeed, in biology,

592
00:22:18,720 --> 00:22:19,940
we we are faced

593
00:22:20,880 --> 00:22:23,299
all over the place, with the fact that

594
00:22:23,599 --> 00:22:25,279
when we give a name to an entity

595
00:22:25,279 --> 00:22:28,319
like a species, actually, this species is changing

596
00:22:28,319 --> 00:22:29,059
over time.

597
00:22:29,599 --> 00:22:32,355
And it is changing in terms of, the

598
00:22:32,355 --> 00:22:34,595
the composition of the gene pool, in the

599
00:22:34,595 --> 00:22:35,095
species,

600
00:22:35,714 --> 00:22:37,575
and then species can branch

601
00:22:37,954 --> 00:22:39,575
and produce new species.

602
00:22:40,515 --> 00:22:42,535
So this is particularly

603
00:22:43,555 --> 00:22:45,474
it's hard to model, but at the same

604
00:22:45,474 --> 00:22:45,974
time,

605
00:22:46,309 --> 00:22:49,190
it can, there are lots of models that

606
00:22:49,190 --> 00:22:50,410
take this into account.

607
00:22:50,950 --> 00:22:53,750
What is, difficult to model is that we

608
00:22:53,750 --> 00:22:55,049
cannot really predict,

609
00:22:55,429 --> 00:22:56,970
what will be the effect

610
00:22:57,269 --> 00:22:58,009
of mutations.

611
00:22:58,625 --> 00:23:01,684
So this is again is our stochastic processes

612
00:23:02,384 --> 00:23:03,505
that allow us,

613
00:23:03,984 --> 00:23:05,525
or nature to explore

614
00:23:06,305 --> 00:23:07,845
a number of different phenotypes

615
00:23:08,625 --> 00:23:11,525
and a number potentially of different communities

616
00:23:12,065 --> 00:23:12,805
or species,

617
00:23:13,570 --> 00:23:16,549
but we cannot really say which will be

618
00:23:17,170 --> 00:23:19,350
the new species that will appear.

619
00:23:19,890 --> 00:23:21,509
And this is somehow

620
00:23:22,450 --> 00:23:22,950
problematic

621
00:23:23,490 --> 00:23:26,450
if you want to be sure that the

622
00:23:26,450 --> 00:23:26,950
novelty

623
00:23:27,330 --> 00:23:27,830
obeys

624
00:23:28,605 --> 00:23:29,585
some given,

625
00:23:30,285 --> 00:23:30,785
law.

626
00:23:31,884 --> 00:23:32,865
At the same time,

627
00:23:33,325 --> 00:23:34,065
if one

628
00:23:34,765 --> 00:23:35,265
considers,

629
00:23:35,804 --> 00:23:36,865
that, mutations

630
00:23:37,244 --> 00:23:37,744
happen

631
00:23:38,605 --> 00:23:39,105
gradually

632
00:23:39,404 --> 00:23:42,065
so that, you hardly ever have

633
00:23:42,859 --> 00:23:43,359
immense,

634
00:23:43,740 --> 00:23:45,840
jumps, in the phenotypic space,

635
00:23:46,380 --> 00:23:47,680
then you can still

636
00:23:48,380 --> 00:23:51,600
try and describe an evolutionary process in terms

637
00:23:51,660 --> 00:23:53,759
of what is the expected distribution

638
00:23:54,460 --> 00:23:55,964
of the phenotypic effects

639
00:23:56,444 --> 00:23:57,105
of mutations.

640
00:23:57,724 --> 00:23:59,585
And then in this way, you can

641
00:24:00,204 --> 00:24:00,704
introduce,

642
00:24:01,644 --> 00:24:02,464
new species.

643
00:24:03,085 --> 00:24:05,244
Then you you need the tools that are

644
00:24:05,244 --> 00:24:07,984
a bit different. So, they they are available,

645
00:24:08,125 --> 00:24:10,544
like, I don't know, adaptive dynamics, for instance.

646
00:24:11,059 --> 00:24:13,880
It is a method that takes into account,

647
00:24:14,819 --> 00:24:17,640
whether a new species that appears will invade

648
00:24:17,859 --> 00:24:19,640
a system or will not.

649
00:24:20,579 --> 00:24:23,000
There are methods from population genetics,

650
00:24:23,744 --> 00:24:24,884
quantitative genetics.

651
00:24:25,424 --> 00:24:25,924
So

652
00:24:26,945 --> 00:24:28,704
the the I I would say that these

653
00:24:28,704 --> 00:24:29,204
methods

654
00:24:29,825 --> 00:24:31,765
have not as yet been

655
00:24:32,144 --> 00:24:33,924
systematically applied to communities.

656
00:24:34,305 --> 00:24:35,605
So they are more often

657
00:24:36,130 --> 00:24:38,150
applied to organisms,

658
00:24:39,409 --> 00:24:40,150
which have,

659
00:24:40,529 --> 00:24:42,950
if you want to also within an organism,

660
00:24:43,009 --> 00:24:44,769
there is a sort of an ecosystem of

661
00:24:44,769 --> 00:24:45,269
genes.

662
00:24:46,289 --> 00:24:48,210
So there is a hope that they can

663
00:24:48,210 --> 00:24:50,470
be actually, they are starting to be

664
00:24:50,954 --> 00:24:51,674
now generalized,

665
00:24:52,154 --> 00:24:54,414
to account also for the evolution of communities.

666
00:24:55,595 --> 00:24:58,095
Recently, there have been studies in which,

667
00:24:58,794 --> 00:25:00,174
people try to,

668
00:25:00,714 --> 00:25:03,534
model couplings and interaction not as sequential

669
00:25:04,075 --> 00:25:07,890
variables, but also as annealed variables, so varying

670
00:25:07,890 --> 00:25:08,549
in time.

671
00:25:08,849 --> 00:25:10,529
And so this can be of interest in

672
00:25:10,529 --> 00:25:13,910
many different systems, so in aquatic systems, plantonic

673
00:25:14,049 --> 00:25:14,549
community.

674
00:25:15,490 --> 00:25:18,309
Sylvia has already discussed this kind of subject.

675
00:25:18,369 --> 00:25:21,195
But, since I have a background in spin

676
00:25:21,195 --> 00:25:23,134
glass theory and this other system,

677
00:25:23,515 --> 00:25:25,055
I would like also to

678
00:25:25,674 --> 00:25:27,875
add that this kind of techniques turn out

679
00:25:27,875 --> 00:25:28,734
to be particularly

680
00:25:29,035 --> 00:25:29,535
powerful

681
00:25:29,994 --> 00:25:30,734
for modeling

682
00:25:31,275 --> 00:25:31,775
gut,

683
00:25:32,779 --> 00:25:36,059
microbial communities. And so very recently, we have

684
00:25:36,059 --> 00:25:37,839
tried to to better understand

685
00:25:38,140 --> 00:25:39,519
the the emergent patterns

686
00:25:39,900 --> 00:25:41,039
in healthy versus

687
00:25:41,500 --> 00:25:42,000
disease

688
00:25:42,380 --> 00:25:46,079
individuals, disease communities. So for instance, community characterized

689
00:25:46,220 --> 00:25:46,694
by,

690
00:25:47,255 --> 00:25:50,394
affected by Crohn disease or ulcerative colitis.

691
00:25:50,774 --> 00:25:52,154
And so the gut microbiota

692
00:25:52,694 --> 00:25:55,734
is a typical instance, a typical example of

693
00:25:55,734 --> 00:25:57,755
system in which you can distinguish

694
00:25:58,855 --> 00:26:00,634
two different time scales.

695
00:26:01,130 --> 00:26:04,090
One, a fast time scale which is driven

696
00:26:04,090 --> 00:26:04,590
by,

697
00:26:05,529 --> 00:26:09,150
daily feeding with and circadian plots

698
00:26:09,610 --> 00:26:11,549
that set the scale over

699
00:26:11,850 --> 00:26:12,825
twenty four hours

700
00:26:14,585 --> 00:26:17,784
and slower time scales that are modulated by

701
00:26:17,784 --> 00:26:18,924
seasonal changes,

702
00:26:19,704 --> 00:26:20,845
diet variation,

703
00:26:21,384 --> 00:26:22,444
and overall

704
00:26:22,744 --> 00:26:23,964
long term stability.

705
00:26:24,585 --> 00:26:26,044
So, again, this is

706
00:26:26,359 --> 00:26:29,000
an open question in theoretical ecology. It's a

707
00:26:29,000 --> 00:26:30,539
very challenging topic,

708
00:26:31,080 --> 00:26:33,740
which we are trying to address from

709
00:26:34,119 --> 00:26:35,340
a theoretical perspective.

710
00:26:36,119 --> 00:26:37,559
And one of the most exciting areas in

711
00:26:37,559 --> 00:26:39,240
medicine as well. Sylvia, do you have some

712
00:26:39,240 --> 00:26:40,299
of that? Yeah. I agree.

713
00:26:41,204 --> 00:26:42,644
Yes. Just thinking,

714
00:26:43,684 --> 00:26:44,184
so

715
00:26:44,484 --> 00:26:45,924
that I would like to point out that

716
00:26:45,924 --> 00:26:46,585
there is

717
00:26:47,125 --> 00:26:48,585
one particularly interesting

718
00:26:48,884 --> 00:26:52,085
topic in a community evolution. That is what

719
00:26:52,085 --> 00:26:53,704
happens when you apply

720
00:26:54,369 --> 00:26:56,289
selection at multiple levels,

721
00:26:56,609 --> 00:26:58,849
at the same time. So you can consider

722
00:26:58,849 --> 00:27:01,429
for instance, in the gut, of course, bacteria

723
00:27:01,730 --> 00:27:03,669
compete against one another.

724
00:27:04,130 --> 00:27:06,390
And, there is a short timescale,

725
00:27:06,929 --> 00:27:07,829
that is associated

726
00:27:08,210 --> 00:27:11,615
with ecological and also evolutionary changes that are

727
00:27:11,615 --> 00:27:12,115
driven

728
00:27:12,575 --> 00:27:13,394
by competition.

729
00:27:14,095 --> 00:27:16,095
But then at the same time, they get

730
00:27:16,095 --> 00:27:18,974
selected because they are in our gut, so

731
00:27:18,974 --> 00:27:19,715
they provide

732
00:27:20,095 --> 00:27:22,109
a function to the host.

733
00:27:23,069 --> 00:27:24,450
So this can be reproduced

734
00:27:24,750 --> 00:27:26,849
also in the lab if you have,

735
00:27:27,630 --> 00:27:28,130
microcosps

736
00:27:28,589 --> 00:27:29,490
that contain,

737
00:27:29,950 --> 00:27:31,009
different species,

738
00:27:31,390 --> 00:27:32,529
and then you can

739
00:27:33,230 --> 00:27:34,690
score these microcosps

740
00:27:35,309 --> 00:27:36,769
according to a given

741
00:27:37,424 --> 00:27:39,845
collective feature. I don't know, total biomass.

742
00:27:41,025 --> 00:27:42,164
And this is something,

743
00:27:42,545 --> 00:27:43,285
that is,

744
00:27:43,985 --> 00:27:45,904
now that there are people who are developing

745
00:27:45,904 --> 00:27:46,644
these methods,

746
00:27:47,105 --> 00:27:49,200
in the hope of, being able

747
00:27:50,079 --> 00:27:52,899
to select communities that have this,

748
00:27:53,519 --> 00:27:54,899
some particular property

749
00:27:55,519 --> 00:27:57,700
like, they are useful for bioremediation

750
00:27:58,639 --> 00:28:00,980
or they produce a particular product.

751
00:28:02,144 --> 00:28:04,224
So what is challenging in this case is

752
00:28:04,224 --> 00:28:07,105
that, you are selecting also at the level

753
00:28:07,105 --> 00:28:08,244
of the communities.

754
00:28:08,785 --> 00:28:09,585
So there is,

755
00:28:10,464 --> 00:28:12,085
competition between communities

756
00:28:13,585 --> 00:28:16,565
for a feature that is a collective feature.

757
00:28:17,130 --> 00:28:17,789
So this,

758
00:28:18,570 --> 00:28:20,750
kind of multilevel selection

759
00:28:22,170 --> 00:28:23,070
is, really,

760
00:28:24,570 --> 00:28:27,769
very actively researched upon, in this moment, and

761
00:28:27,769 --> 00:28:28,910
mathematical models,

762
00:28:29,505 --> 00:28:31,845
including models from statistical physics

763
00:28:32,305 --> 00:28:34,244
are being used to try and understand

764
00:28:34,625 --> 00:28:35,684
under what conditions

765
00:28:36,144 --> 00:28:36,644
can

766
00:28:37,105 --> 00:28:37,924
this evolution

767
00:28:38,305 --> 00:28:39,525
be most efficient?

768
00:28:39,825 --> 00:28:40,884
When does it fail

769
00:28:41,184 --> 00:28:41,585
and,

770
00:28:42,065 --> 00:28:43,285
how to actually

771
00:28:43,759 --> 00:28:44,899
obtain communities,

772
00:28:45,679 --> 00:28:46,419
that provide,

773
00:28:46,799 --> 00:28:47,940
a specific function.

774
00:28:48,559 --> 00:28:49,679
And this would presumably

775
00:28:50,079 --> 00:28:52,740
potentially in the future help someone say, okay.

776
00:28:52,799 --> 00:28:54,659
So this person has symptoms

777
00:28:54,960 --> 00:28:58,319
of irritable bowel syndrome, IBS, Crohn's disease, or

778
00:28:58,319 --> 00:28:59,379
something like that.

779
00:28:59,734 --> 00:29:00,875
And we've seen

780
00:29:01,575 --> 00:29:04,215
these conditions in their guts, and we think

781
00:29:04,215 --> 00:29:06,535
that changing the system in some way would

782
00:29:06,535 --> 00:29:08,934
help the system the ecosystem in their gut

783
00:29:08,934 --> 00:29:11,015
evolve into something that was not the disease

784
00:29:11,015 --> 00:29:14,480
state. Yeah. Yes. Or you might be able

785
00:29:14,480 --> 00:29:17,200
at a certain point to swallow a pill

786
00:29:17,200 --> 00:29:20,000
that has been that contains a community that

787
00:29:20,000 --> 00:29:20,740
was evolved

788
00:29:21,359 --> 00:29:22,480
in order to,

789
00:29:22,960 --> 00:29:23,440
help,

790
00:29:23,759 --> 00:29:25,059
the gut reestablish

791
00:29:26,005 --> 00:29:27,224
its original state.

792
00:29:28,005 --> 00:29:30,724
Yeah. For instance, you can think of performing

793
00:29:30,724 --> 00:29:34,005
a fecal microbiota transplant from a healthy donor

794
00:29:34,005 --> 00:29:35,304
to an healthy recipient

795
00:29:35,765 --> 00:29:37,304
and to study the evolution

796
00:29:37,765 --> 00:29:38,664
of the community

797
00:29:39,849 --> 00:29:41,630
over several months.

798
00:29:42,009 --> 00:29:43,769
In that new environment that has been placed

799
00:29:43,769 --> 00:29:45,369
in as opposed to where it came from.

800
00:29:45,369 --> 00:29:47,929
Yeah. And I will also add that our

801
00:29:47,929 --> 00:29:49,149
gut is a very

802
00:29:49,529 --> 00:29:50,750
structured ecosystem.

803
00:29:51,369 --> 00:29:52,829
So it's it's important

804
00:29:53,450 --> 00:29:54,029
to take

805
00:29:54,490 --> 00:29:54,990
structure

806
00:29:55,505 --> 00:29:58,384
into account in this kind of model. For

807
00:29:58,384 --> 00:30:01,105
instance, by using a meta community scenario in

808
00:30:01,105 --> 00:30:03,285
which different patches are connected

809
00:30:03,985 --> 00:30:05,205
with with the others.

810
00:30:06,305 --> 00:30:08,945
So was it strange for you, Ada, to

811
00:30:08,945 --> 00:30:11,559
go from thinking about spin glasses and the

812
00:30:11,559 --> 00:30:13,980
physics of that to thinking about the statistical

813
00:30:14,119 --> 00:30:15,659
physics of gut microbiomes?

814
00:30:16,599 --> 00:30:17,980
Yeah. It might appear

815
00:30:18,359 --> 00:30:21,480
a quite exotic topic. I have a background

816
00:30:21,480 --> 00:30:25,159
in statistical physics of disorders system, field theory,

817
00:30:25,159 --> 00:30:28,134
and so on. So very recently, in collaboration

818
00:30:28,275 --> 00:30:29,575
with the team of gastroenterologists

819
00:30:30,755 --> 00:30:32,054
in Padua in Italy,

820
00:30:32,515 --> 00:30:34,934
we try to connect disorder systems

821
00:30:35,394 --> 00:30:37,015
akin to spin glasses

822
00:30:37,394 --> 00:30:39,654
that are still in their infancy

823
00:30:40,470 --> 00:30:41,450
with metagenomic

824
00:30:41,829 --> 00:30:45,210
data from healthy versus unhealthy dataset

825
00:30:45,589 --> 00:30:48,950
where for unhealthy immune patients that are affected

826
00:30:48,950 --> 00:30:51,769
by Crohn disease and ulcerative colitis.

827
00:30:52,304 --> 00:30:54,784
So the the kind of strategies that we

828
00:30:54,784 --> 00:30:57,284
perform, that we apply, is basically

829
00:30:57,585 --> 00:30:59,204
based on the following approach.

830
00:30:59,585 --> 00:31:02,164
So we propose some order parameters,

831
00:31:02,944 --> 00:31:05,125
mean abundance and high order correlations

832
00:31:05,585 --> 00:31:08,210
between species abundance that are typically

833
00:31:08,589 --> 00:31:11,890
referred to as overlap, self overlap, and intrastate

834
00:31:12,029 --> 00:31:13,569
overlap in spin glass theory.

835
00:31:13,869 --> 00:31:16,589
We measure this quantity from data for the

836
00:31:16,589 --> 00:31:17,490
two cohorts,

837
00:31:18,029 --> 00:31:20,450
for the two data set, healthy versus unhealthy.

838
00:31:20,924 --> 00:31:23,485
And then in a high dimensional setting, so

839
00:31:23,485 --> 00:31:24,384
based on

840
00:31:24,765 --> 00:31:27,505
the disorder generalized loca paltura model,

841
00:31:27,805 --> 00:31:30,545
we infer a limited set of parameters.

842
00:31:31,005 --> 00:31:33,825
So mean and variance of the interaction matrix,

843
00:31:34,440 --> 00:31:37,579
demographic noise amplitude, and an immigration parameter,

844
00:31:38,200 --> 00:31:39,179
which is crucial

845
00:31:39,720 --> 00:31:43,000
to prevent one species from getting extinct in

846
00:31:43,000 --> 00:31:45,159
in this kind of setting. And so by

847
00:31:45,159 --> 00:31:48,039
inferring this kind of quantity, we could actually

848
00:31:48,039 --> 00:31:48,539
decode

849
00:31:49,184 --> 00:31:50,404
microbiome data,

850
00:31:50,784 --> 00:31:52,884
which offer insight into the ecological

851
00:31:53,264 --> 00:31:54,644
forces that shapes

852
00:31:55,024 --> 00:31:55,524
macroecological

853
00:31:56,065 --> 00:31:58,964
states. And indeed, we managed to classify,

854
00:32:00,144 --> 00:32:02,304
the two data set, the two cohorts for

855
00:32:02,304 --> 00:32:05,920
healthy versus unhealthy, and we could actually have

856
00:32:06,299 --> 00:32:07,359
visible clusterization

857
00:32:07,980 --> 00:32:08,880
of the two.

858
00:32:09,259 --> 00:32:10,160
One characterized

859
00:32:11,019 --> 00:32:11,599
by higher

860
00:32:12,220 --> 00:32:12,720
heterogeneity

861
00:32:13,660 --> 00:32:15,599
and higher demographic fluctuations

862
00:32:16,255 --> 00:32:18,734
in such a way to to interpret this

863
00:32:18,734 --> 00:32:20,515
kind of fundings as,

864
00:32:21,214 --> 00:32:23,714
being more stable with respect to external

865
00:32:24,015 --> 00:32:24,515
perturbation,

866
00:32:25,454 --> 00:32:25,954
antibiotic

867
00:32:26,255 --> 00:32:26,755
treatment,

868
00:32:27,534 --> 00:32:28,034
changes

869
00:32:28,335 --> 00:32:29,234
in the diet,

870
00:32:29,619 --> 00:32:30,919
and another characterized

871
00:32:31,220 --> 00:32:32,200
by smaller

872
00:32:32,579 --> 00:32:33,079
heterogeneity

873
00:32:33,460 --> 00:32:36,679
and smaller demographic noise. And so this could

874
00:32:37,059 --> 00:32:38,039
open the room

875
00:32:38,819 --> 00:32:40,200
for targeted strategies

876
00:32:40,819 --> 00:32:42,279
in microbial dynamics,

877
00:32:42,755 --> 00:32:44,855
also for for better investigating

878
00:32:45,235 --> 00:32:48,434
the appearance of multiple alternative stable states in

879
00:32:48,434 --> 00:32:51,174
this kind of community. Because indeed, an healthy

880
00:32:51,394 --> 00:32:53,495
dataset seems to be more

881
00:32:53,955 --> 00:32:54,455
captured

882
00:32:55,099 --> 00:32:57,340
and seems to get closer and closer to

883
00:32:57,340 --> 00:33:00,160
the stability line of the single equilibrium regime.

884
00:33:00,299 --> 00:33:02,940
So there is still room for discussion, and

885
00:33:02,940 --> 00:33:05,180
we would like to work on this kind

886
00:33:05,180 --> 00:33:08,299
of topics in the next few months and

887
00:33:08,299 --> 00:33:09,194
and few years.

888
00:33:10,474 --> 00:33:12,794
At the end of your EPL paper, you

889
00:33:12,794 --> 00:33:15,274
expressed some hope that statistical physics can bring

890
00:33:15,274 --> 00:33:18,654
ideas not only for officially describing currently available

891
00:33:18,714 --> 00:33:19,214
observations,

892
00:33:19,914 --> 00:33:22,255
but also for planning future ones.

893
00:33:22,769 --> 00:33:25,490
How might your ecologist or biologist or even

894
00:33:25,490 --> 00:33:26,390
medical colleagues

895
00:33:26,930 --> 00:33:28,869
use the finding of statistical physics

896
00:33:29,170 --> 00:33:30,950
to plan future observations?

897
00:33:32,609 --> 00:33:35,670
Yeah. Our hope is that statistical physics approach

898
00:33:36,205 --> 00:33:37,744
and intuition from

899
00:33:38,125 --> 00:33:40,144
disorder system cannot prioritize

900
00:33:40,845 --> 00:33:44,545
observation that are most informative about underlying

901
00:33:44,845 --> 00:33:46,144
ecological mechanisms.

902
00:33:46,605 --> 00:33:49,965
We mentioned Crohn disease and ulcerative colitis. So

903
00:33:49,965 --> 00:33:50,465
can

904
00:33:50,940 --> 00:33:53,759
statistical physics can definitely help in

905
00:33:54,380 --> 00:33:54,880
providing

906
00:33:55,500 --> 00:33:59,740
quantitative fingerprints, quantitative order parameters for distinguished different

907
00:33:59,740 --> 00:34:02,079
regimes. So I will say, first of all,

908
00:34:02,299 --> 00:34:03,839
can be very efficient

909
00:34:04,214 --> 00:34:06,714
in detecting phase transition and criticality,

910
00:34:07,255 --> 00:34:08,155
and ecologists

911
00:34:08,534 --> 00:34:11,835
could plan experiments along environmental

912
00:34:12,135 --> 00:34:12,635
gradients

913
00:34:13,014 --> 00:34:15,275
or under control perturbations,

914
00:34:16,295 --> 00:34:19,355
like increasing variability in species abundance,

915
00:34:20,190 --> 00:34:21,409
tuning, pH

916
00:34:21,789 --> 00:34:22,289
level,

917
00:34:22,670 --> 00:34:25,650
or nutrient amount in a well controlled

918
00:34:26,190 --> 00:34:29,409
chemical condition, for instance, in a chemostat reactor.

919
00:34:30,109 --> 00:34:31,730
Second, probing multistability

920
00:34:33,309 --> 00:34:34,369
with replicated

921
00:34:34,670 --> 00:34:35,170
samples.

922
00:34:35,815 --> 00:34:39,275
So we mentioned several times single temporal snapshot

923
00:34:39,574 --> 00:34:40,074
versus

924
00:34:40,454 --> 00:34:40,954
longitudinal

925
00:34:41,335 --> 00:34:42,954
data, long time series,

926
00:34:43,414 --> 00:34:44,235
and also

927
00:34:45,094 --> 00:34:47,275
trying to reduce the dimensionality

928
00:34:47,815 --> 00:34:48,635
of the model

929
00:34:48,940 --> 00:34:50,239
and to have information

930
00:34:50,699 --> 00:34:51,440
on the community

931
00:34:52,140 --> 00:34:53,280
just based on

932
00:34:53,579 --> 00:34:54,800
a few parameters.

933
00:34:55,739 --> 00:34:56,239
So

934
00:34:57,019 --> 00:34:59,519
I will say that these are three main

935
00:34:59,579 --> 00:35:00,079
directions.

936
00:35:00,539 --> 00:35:02,855
So this can open the road to have

937
00:35:02,855 --> 00:35:05,275
controlled strategies in many different

938
00:35:05,574 --> 00:35:07,034
ecological and biological

939
00:35:07,335 --> 00:35:07,835
systems.

940
00:35:09,014 --> 00:35:10,954
I think that, yeah, the main

941
00:35:11,574 --> 00:35:12,074
goal

942
00:35:12,454 --> 00:35:14,954
of our work is to be able to

943
00:35:15,494 --> 00:35:16,635
propose to ecologists

944
00:35:17,174 --> 00:35:17,674
some

945
00:35:18,710 --> 00:35:20,809
order parameters or some statistical,

946
00:35:22,309 --> 00:35:22,789
means,

947
00:35:23,269 --> 00:35:24,089
of distinguishing,

948
00:35:24,789 --> 00:35:25,690
different processes.

949
00:35:26,630 --> 00:35:29,449
So I wouldn't say that so far,

950
00:35:30,385 --> 00:35:30,885
ecologists

951
00:35:31,505 --> 00:35:34,385
would come to us and ask, okay. What

952
00:35:34,385 --> 00:35:34,885
is

953
00:35:35,184 --> 00:35:37,204
the other parameter I should look at?

954
00:35:37,985 --> 00:35:40,305
But I think that there have been, so

955
00:35:40,305 --> 00:35:42,885
many progresses in the last few years,

956
00:35:43,265 --> 00:35:46,719
that the news are now starting to percolate

957
00:35:46,940 --> 00:35:47,440
also

958
00:35:48,139 --> 00:35:49,039
in the ecological

959
00:35:49,340 --> 00:35:49,840
community.

960
00:35:50,539 --> 00:35:51,920
Though it is challenging,

961
00:35:52,619 --> 00:35:54,639
to communicate across disciplines

962
00:35:55,340 --> 00:35:57,420
and to be able to reach out to

963
00:35:57,420 --> 00:35:59,119
the people who actually plan

964
00:35:59,715 --> 00:36:01,095
the observational campaigns,

965
00:36:01,875 --> 00:36:04,135
which are often planned on considerations

966
00:36:04,434 --> 00:36:06,454
that are different, from, those,

967
00:36:07,315 --> 00:36:07,815
theorists

968
00:36:08,275 --> 00:36:10,914
for good reasons. I am not saying they

969
00:36:10,914 --> 00:36:13,335
shouldn't, but we we will, anyway,

970
00:36:14,090 --> 00:36:15,929
get more and more data because this is

971
00:36:15,929 --> 00:36:19,130
the trend. There's more and more observational data

972
00:36:19,130 --> 00:36:22,190
that will be available and that are

973
00:36:22,890 --> 00:36:23,690
more and more,

974
00:36:24,570 --> 00:36:26,269
available in public repositories.

975
00:36:27,210 --> 00:36:27,855
So this,

976
00:36:29,054 --> 00:36:30,994
will make our work easier.

977
00:36:31,534 --> 00:36:32,914
What are some of the challenges

978
00:36:33,215 --> 00:36:36,094
of connecting with the data that's gathered by

979
00:36:36,094 --> 00:36:38,914
observational scientists for you as a theoretical ecologist?

980
00:36:40,094 --> 00:36:41,394
Yeah. So there are several

981
00:36:41,775 --> 00:36:42,275
challenges.

982
00:36:43,380 --> 00:36:45,319
First of all, there are differences,

983
00:36:46,019 --> 00:36:47,000
in perspective,

984
00:36:47,539 --> 00:36:49,400
even over the same data,

985
00:36:50,019 --> 00:36:51,880
from people coming from physics

986
00:36:52,179 --> 00:36:53,779
and people coming more from,

987
00:36:54,659 --> 00:36:55,880
experimental ecology.

988
00:36:57,174 --> 00:36:58,074
So there is

989
00:36:58,454 --> 00:36:59,194
a trend,

990
00:36:59,734 --> 00:37:00,234
in

991
00:37:00,614 --> 00:37:03,414
the recent decades that has been spurred by

992
00:37:03,414 --> 00:37:03,994
the availability

993
00:37:04,295 --> 00:37:05,034
of methods

994
00:37:05,815 --> 00:37:08,554
of sequencing that are more and more sophisticated

995
00:37:08,934 --> 00:37:10,554
and increasingly cheaper

996
00:37:11,690 --> 00:37:12,829
to actually collect

997
00:37:13,130 --> 00:37:15,529
a lot a lot of data and then

998
00:37:15,529 --> 00:37:16,029
proceed

999
00:37:16,329 --> 00:37:18,109
with statistical approaches,

1000
00:37:18,889 --> 00:37:21,389
mining this data and trying to find out

1001
00:37:21,690 --> 00:37:22,429
what are

1002
00:37:22,969 --> 00:37:23,630
the signals

1003
00:37:24,335 --> 00:37:26,355
of, I don't know, variation,

1004
00:37:26,894 --> 00:37:27,394
from

1005
00:37:28,014 --> 00:37:29,315
one place to another

1006
00:37:29,694 --> 00:37:30,514
of ecosystems.

1007
00:37:31,614 --> 00:37:34,734
Our point of view is typically to start

1008
00:37:34,734 --> 00:37:35,875
from a hypothesis,

1009
00:37:36,734 --> 00:37:39,099
and this is also true for a lot

1010
00:37:39,099 --> 00:37:40,559
of theoretical biology.

1011
00:37:41,420 --> 00:37:44,300
You start from hypothesis and then you would

1012
00:37:44,300 --> 00:37:46,860
like data to be gathered to support or

1013
00:37:46,860 --> 00:37:47,760
not this hypothesis.

1014
00:37:48,860 --> 00:37:50,880
And, the two things are complimentary,

1015
00:37:51,659 --> 00:37:54,934
but, sometimes there is like a language barrier

1016
00:37:55,234 --> 00:37:58,694
and the the perspective barrier that makes it

1017
00:37:59,234 --> 00:38:00,054
not completely

1018
00:38:01,394 --> 00:38:02,755
easy for us,

1019
00:38:03,074 --> 00:38:03,574
to

1020
00:38:04,114 --> 00:38:06,054
speak to an ecological

1021
00:38:06,594 --> 00:38:07,094
audience.

1022
00:38:07,949 --> 00:38:10,530
And this part of this, is that

1023
00:38:11,230 --> 00:38:11,730
ecology

1024
00:38:12,030 --> 00:38:13,329
has a long history,

1025
00:38:14,190 --> 00:38:16,989
and there are, schools of thought that are

1026
00:38:16,989 --> 00:38:18,289
rooted in this history.

1027
00:38:18,829 --> 00:38:20,449
And we do not necessarily

1028
00:38:21,309 --> 00:38:21,809
know

1029
00:38:22,164 --> 00:38:24,264
all of this, coming from physics,

1030
00:38:24,804 --> 00:38:27,125
even though I personally have been, in a

1031
00:38:27,125 --> 00:38:29,864
college and evolution of almost forever now.

1032
00:38:30,244 --> 00:38:32,484
But still, I feel like I am a

1033
00:38:32,484 --> 00:38:32,984
physicist,

1034
00:38:33,364 --> 00:38:33,864
and

1035
00:38:34,565 --> 00:38:37,519
the perspective I bring on the data is

1036
00:38:37,519 --> 00:38:38,739
more that of a physicist

1037
00:38:39,119 --> 00:38:41,139
than, of an ecologist.

1038
00:38:42,000 --> 00:38:44,239
I suppose it takes all perspective to see

1039
00:38:44,239 --> 00:38:47,119
the entire problem and to solve it. Hopefully

1040
00:38:47,119 --> 00:38:49,119
so. That's the effort that we are trying

1041
00:38:49,119 --> 00:38:51,460
to do with, ADA also engaging,

1042
00:38:52,000 --> 00:38:53,175
with medical doctors.

1043
00:38:54,295 --> 00:38:55,755
Doctors. Yeah. That's the hope.

1044
00:38:56,855 --> 00:38:59,015
Well, Sylvia and Ada, thank you very much

1045
00:38:59,015 --> 00:39:00,855
for coming on the podcast. It's been wonderful

1046
00:39:00,855 --> 00:39:01,835
speaking with you.

1047
00:39:02,135 --> 00:39:04,795
Thank you very much for having us, and,

1048
00:39:05,414 --> 00:39:06,315
all the best.

1049
00:39:14,680 --> 00:39:17,180
That was Ada Altieri and Silvia de Monte

1050
00:39:17,400 --> 00:39:20,619
talking about statistical physics approaches to ecological communities.

1051
00:39:20,945 --> 00:39:22,304
If you'd like to know more about this

1052
00:39:22,304 --> 00:39:24,625
work, check out their paper in EPL, which

1053
00:39:24,625 --> 00:39:27,025
is open access and freely available via the

1054
00:39:27,025 --> 00:39:27,525
IOPscience

1055
00:39:27,824 --> 00:39:28,324
website.

1056
00:39:29,824 --> 00:39:31,824
My thanks to Ade and Sylvia, to our

1057
00:39:31,824 --> 00:39:34,699
producer Fred Iles, and to the journal EPL,

1058
00:39:34,760 --> 00:39:37,079
which sponsored this episode of the Physics World

1059
00:39:37,079 --> 00:39:37,900
weekly podcast.

1060
00:39:38,840 --> 00:39:41,019
Join us again next week for more fascinating

1061
00:39:41,159 --> 00:39:42,699
stories from the world of physics.