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1+ paragraphs on any combination of the following:
'''Questions from my talk:'''
True or false?
1. Classical ecological models are inadequate for understanding microbial ecosystems.
2. The large-scale, reproducible patterns we see across microbiomes are emergent features of “typical random ecosystems.”
3. Diverse communities will almost always behave like “random ecosystems.”
'''Question I want to discuss'''
Can large-scale ecological changes over time be understood through simple general principles? (Samraat, Jacopo, Priyanga, me: flux balance/metabolic rates, random matrix theory, biochemical and morphological constraints)
Are there ecological summary statistics that change monotonically over time? (Jacopo, and some of my work that I didn't talk about -- see references)
Which properties of individual organisms are essential for predicting large-scale ecological changes?
- Pamela -- why do some vaccines fail to produce large-scale ecological change (i.e., pathogen extinction)?
- Greg -- importance of distribution of susceptibilities to dynamics of epizootic onset.
- Fernanda -- importance of adaptive foraging for understanding why pollinator systems don't collapse -- and why they might under new circumstances.
- Samraat -- taking the mechanics of inter-species interaction seriously, understanding how they affect response of ecosystem to temperature changes.
- Priyanga -- asymmetry of reaction norms for temperature variation.
What do we miss when we focus too much on equilibrium/steady states? (Prevalence of cyclic disturbance/recovery dynamics -- Greg, Samraat)
"The Minimum Environmental Perturbation Principle" (Marsland et al. 2019) is some new work I didn't include in my presentation, but which is possibly more relevant to the irreversibility theme. The main result is that a wide class of niche models exhibit monotonic increase in the environmental perturbation under successive invasions/evolution. I would love any feedback from the ecologists about references to add, things that are unclear, etc.
Marsland and England 2017 and Marsland ''et al.'' 2015 contain in-depth explanations of the two kinds of “thermodynamic” irreversibility I wrote up on the board.
Mehta ''et al.'' 2018 begins with a discussion of the "bias/variance tradeoff", which is extremely relevant to the use of models with many parameters to make predictions. It also has a section on dimensional reduction and clustering that might be useful to people working with high-dimensional phenotype data. (Note that the wiki didn't allow me to enter the whole author list, which should also include Marin Bukov, Charles Fisher and David Schwab.)
Momeni ''et al.'' 2017 shows some of the ways in which Lotka-Volterra can fail to capture the population dynamics of a generalized class of consumer-resource models.
Fisher and Mehta 2014 shows how both niche and neutral regimes can arise in Lotka-Volterra dynamics with immigration, depending on the parameter values.
"Available Energy Fluxes..." (Marsland et al. 2019) contains a full explanation of our microbial consumer resource model in the appendix. The Python implementation can be found at our group github: https://github.com/Emergent-Behaviors-in-Biology/community-simulator.
I have also included the original Human Microbiome Project and Earth Microbiome Project data papers, which contain the large-scale patterns I was showing in the presentation.
Gutenknust ''et al.'' 2007 explains some of the subtleties of Bayesian model fitting in a very accessible way, and strongly influenced the way I think about many-parameter models.
Goldford ''et al.'' 2018 contains some of the patterns I was talking about at the beginning of my talk, which are already captured by a preliminary version of the model.