Property:Reference material notes
From Complex Time
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"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.
'''Temperature-dependence and size scaling of Microbial metabolism'''
The question of the temperature-dependence of bacterial metabolism came up in multiple talks. This manuscript from our lab provides some general empirical insights into this: https://www.biorxiv.org/content/10.1101/524264v1.abstract
'"`UNIQ--nowiki-000008A5-QINU`"'This paper on size-scaling might also be relevant to some: 10.1073/pnas.1007783107
'''Natural experiments on effect of temperature on ecosystem structure and function'''
Some relevant papers:
# Yvon-Durocher, G., Allen, A. & Cellamare, M. Five Years of Experimental Warming Increases the Biodiversity and Productivity of Phytoplankton. ''PLoS Biol'' (2015).
# Dossena, M. ''et al.'' Warming alters community size structure and ecosystem functioning. ''Proc. R. Soc. B Biol. Sci.'' '''279,''' 3011–3019 (2012).
# Schaum, C.-E. ''et al.'' Adaptation of phytoplankton to a decade of experimental warming linked to increased photosynthesis. ''Nat. Ecol. Evol.'' '''1,''' 0094 (2017).
# Yvon-Durocher, G., Montoya, J. M., Woodward, G., Jones, J. I. & Trimmer, M. Warming increases the proportion of primary production emitted as methane from freshwater mesocosms. ''Glob. Chang. Biol.'' '''17,''' 1225–1234 (2011).
'''Assembly/Succession/Evolution of microbial/bacterial networks/communities'''
1. Lawrence, D. ''et al.'' Species interactions alter evolutionary responses to a novel environment. ''PLoS Biol.'' '''10,''' e1001330 (2012).
2. Rivett, D. W. ''et al.'' Elevated success of multispecies bacterial invasions impacts community composition during ecological succession. ''Ecology Letters'' '''21,''' 516–524 (2018). +
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* Baker et al. Nature, 2011: Paper from Jan van Deursen's lab on delaying (reversing?) aging by clearing senescent cells in a mouse.
* Zhang et al. Cell Syst., 2016: 3 different models for aging in c elegans with evidence for 2 of the models, from Zach Pincus's lab
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* Braun et al. 2019 formalizes a notion of the energy of brain state transitions constrained by the underlying anatomical network architecture. The study also demonstrates that the energy to persist in a cognitively demanding state is modulated by dopamine and altered in schizophrenia.
* Lynn et al. 2019 develops an analytical framework to study the information generated by a system as perceived by human observers, who collectively process this information in inefficient and biased ways. Our findings suggest that many real networks are constrained by the pressures of information transmission to and among biased humans, and that these pressures select for specific structural features.
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* D. Yamins: This paper lays out the approach of using task-driven modeling to predict neuronal signals, and more generally describes a novel and very different way of thinking about how to characterize brain function using computational models.
* Avena-Koenigsberger et al (2017): This paper is the first that I know of to discuss seriously the relationship between network communication and brain computation.
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* Gratton et al. 2019 Cereb Cortex.
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* McIntosh & Jirsa 2019 present a dynamical systems framework - Structured Flows on Manifolds - that posits that neural processes are flows depicting system interactions that occur on relatively low-dimension manifolds, which constrain possible functional configurations. Such constraints allow us to characterize the actual and potential configurations of brain networks and provide a new perspective wherein behavior deficits from pathological processes could be either the emergence of an existing repertoire or the adaptation of the system to damage.
* Corbetta et al 2018 propose that large-scale nerwork abnormalities following a stroke reduce the variety of neural states visited during task processing and at rest, resulting in a limited repertoire of behavioral states. The emphasis here is on the changes in the dimensionality of brain and behavior dynamics and whether explicitly linking the two would provide a better characterization of the deficits and adaptation following stroke.
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* Meisel et al. 2017 demonstrates that sleep deprivation associated with rapid cognitive decline correlates with a deviation from critical dynamics quantified in the change in long-term temporal correlations or critical slowing down.
* Seshadri et al. 2018: using an animal model for schizophrenia, it is shown that a hallmark of the disease – loss of working memory – correlates with deviation from avalanche dynamics. Memory performance and critical dynamics can be acutely rescued with the NMDA receptor agonist D-serine.
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* Poeppel D. 2012 nicely lays out one of the central challenges of using brain data to understand mind and behavior: the elements of psychological models are incommensurate with brain measurements. Failure to recognize this problem has hobbled cognitive neuroscience and its applications to medicine.
* Huth et al. 2016 (from the Gallant group) shows how high-dimensional functional mapping can be performed in single individuals, and how we can predict individualized functional maps using a statistical model that reflects the variance and covariance of brain anatomy and brain function across individuals.
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* Translation in cognitive neuroscience remains beyond the horizon, brought no closer by claimed major advances in our understanding of the brain. Nachev ''et al''., propose that adequate individualisation, needed for accurate diagnosis, requires models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms.
* A proposal that, in the next quarter century, advances in “cartography” will result in progressively more accurate drafts of a data-led, multi-scale model of normal, abnormal and even adapting, whole human brain structure and function. These draft blueprints will result from analysis of large volumes of neuroscientific and clinical data, by an iterative process of reconstruction, modelling and simulation.
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* Warren et al. 2014 discusses a case where network models of the brain may help to provide information about behavioral disruptions after brain damage.
* Gratton et al. 2018 reviews aspects of the forms of variation available in functional MRI measurements, which may constrain which types of questions different fMRI measures are best suited to addressing.
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* Wittmann M. 2015 is a good review on modulators of time perception.
* The Morandi et al. 2017 outlines a common clinical scenario (acute brain failure) complicating medical care in aging patients.
* Hasenkamp and Barsalou 2012 article puts a systems neuroscience framework over volitional control of focusing attention.
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- Recommended for this course (1 & 2):
1) Lee, CT, and S Tuljapurkar. 2011. Quantitative, dynamic models to integrate environment, population, and society. Pages 111-133 in Kirch, PV, ed. Roots of Conflict: Soils, Agriculture, and Sociopolitical Complexity in Ancient Hawai'i. School of Advanced Research Press, Santa Fe, New Mexico.
''This book chapter summarizes the effort to integrate models for the environment and environment-dependent demography that is the focus of my lecture during the course. It's intended as an introduction to and overview of the dynamic modeling approach--details are there for folks who are interested, but not necessary.''
2) Lee, CT, S Tuljapurkar, and P Vitousek. 2006. Risky business: spatial and temporal variation in preindustrial dryland agriculture. Human Ecology 34 (6): 739-763
''This paper goes into more detail on the environmental modeling and is optional for that reason, but its introduction does a bit better job than the book chapter of setting up the context and larger questions framing the work.''
- Supplementary readings for more detail on other parts of the project (3 - 6):
3) Lee and Tuljapurkar 2008 details food-dependent demographic dynamics when populations are in a phase of long-term exponential growth.
4) Puleston and Tuljpurkar 2008 give details of how demography changes when total land area begins to limit population growth.
5) Lee et al. 2009 examine both growing and space-limited populations with environmental variability.
6) Ladefoged et al. 2008 explains the application of the coupled model to questions about social organization. +
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1) Haworth et al show that heritability of a well-defined measure of cognition (hence related to the vaguer concept of IQ) changes with age. Such studies are more reliable than GWAS modeling.
2) Steiner & me show that there is lot of non-genetic heterogeneity in complex life cycles, and how to compute it
3) Steinsaltz & Evans show that stochastic"reliability" models of complex systems do NOT lead to particular "generic" patterns of failure. E.g., we don't get Gompertz from reliability models. Humans are not cars!
4) Etges et al show that genes act as clustered networks that change with age -- see
Etges, W. J., Trotter, M. V., de Oliveira, C. C., Rajpurohit, S., Gibbs, A. G., and Tuljapurkar, S. (2015). Deciphering life history transcriptomes in different environments. Molecular ecology, 24(1):151–179. +
1) Tenaillon (2014) reviews the insights brought upon by Fisher Geometric model in evolutionary genetics: could it be useful as well for our understanding of aging?
2) Martin (2014) shows how and when Fisher geometric model of adaptation emerges from complex networks of interacting modules
3) Promislow and Moorad (2088) did use that framework to model aging; could it be used to address different questions about aging, dfferent attractors, resilience?
4) tipping points on one side and evolutonary theories of aging on the other have been discussed as distinct frameworks, which should be better connected; how tipping points may be affected by evolution was discussed in a recent review (interested in ecological tipping points mostly); it could be a good starting point to read Dakos et al. (2019)
5) another example of mutation accumulation affecting aging in daphnia in Lohr et al. (2014)
6) still another one on fitness landscapes but combined with data on antibiotic resistance evolution to address questions about how these fitness landscapes change with the environment/stress: check Harmand et al. (2017) +
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Arthur Guyton's computer model of the cardiovascular system
Hummod model, see http://hummod.org
BioGears, see https://www.biogearsengine.com +
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Baker DJ, Childs BG, Durik M, Wijers ME, Sieben CJ, Zhong J, Saltness RA, Jeganathan KB, Verzosa GC, Pezeshki A, Khazaie K, Miller JD, '''van Deursen JM'''. Naturally occurring p16(Ink4a)-positive cells shorten healthy lifespan. Nature. 2016 Feb 11; 530 (7589):184-9 Epub 2016 Feb 03
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=26840489&query_hl=11&itool=pubmed_docsum +
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Borsboom et al. 2019 challenges the idea that reductionist approaches are appropriate for studying complex human neurological disorders and suggests that network approaches might offer alternative conceptualizations explaining dysfunction. Do network approaches offer novel ways to both explain and intervene on “broken” brains? +
Both Newport et al. 2017 and Makin et al. question the idea of pluripotent cortical plasticity early or late in life, i.e, they throw doubt on the idea that areas can take on qualitatively new functions after injury. +
Cabeza et al. (2018) is a consensus opinion paper on of three popular terms in the cognitive neuroscience of aging and dementia, which are all related to the concept of robustness: reserve, maintenance, and compensation. "Reserve" is defined as the cumulative improvement, due to genetic and/ or environmental factors, of neural resources that mitigates the effects of neural decline caused by aging or age-related diseases. "Maintenance" refers to the preservation of neural resources, which entails ongoing repair and replenishment of the brain in response to damage incurred at cellular and molecular levels due to ‘wear and tear.’ Finally, "compensation" refers to the cognition-enhancing recruitment of neural resources in response to relatively high cognitive demand.
Cabeza, Stanley, and Moscovitch (2018) argue that, compared to large-scale networks, cognitive theories are easier to relate to mini-networks called process specific alliances (PSAs). A PSA is small team of brain regions that rapidly assemble to mediate a cognitive process in response to task demands but quickly disassemble when the process is no longer needed. +
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Colleran & Mace 2015 gives an excellent example from rural Poland which examines the relative effect of individual and group level variables on fertility outcomes.
Gurven & Kaplan 2007 discuss longevity among hunter-gatherers, giving us a framework for understanding what human demography may have looked like in our evolutionary past.
Kohler, Behrman & Watkins 2001 shows the effects of different social network structure on contraceptive knowledge and contraceptive use, showing how some may promote social learning while some inhibit it.
Lam 2011 gives important context for concerns about overpopulation in the past, and how many of these concerns were not realized though some were.
Nolin & Ziker 2016 examines a very rapid fertility decline--more of a fertility crash--in Siberia following the collapse of the Soviet Union, emphasizing how abrupt change or high levels of uncertainty may in some cases predict to low fertility. This is also a very elegant statistical model.
Page et al. 2016 gives an empirical test in the modern world of the mechanism by which the Neolithic Demographic Transition may have occurred thousands of years ago.
Shenk et al. 2013 gives a brief review of different causal models of the demographic transition and a comparison among them using model selection methods on detailed data.
Shenk, Kaplan & Hooper 2016 models the effects of status competition and inequality on fertility decisions. Results suggest that the dynamics of social competition may increase the scope of fertility decline compared to economic motivations alone. +
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During Annette Ostling's talk, I mentioned this study, which finds "clusters" in trait space emerging without explicit assumptions about niches. I think Jacopo may find it interesting as well.
https://www.pnas.org/content/114/13/E2719 +
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Ellner and Rees is overview of age-structured models
Espenshade papers introduce momentum
Arrow and Levin introduce notion of intergenerational transfer of resources
Keyfitz and Keyfitz introduce continuous-time models +
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Flack et al. 2012 summarizes our understanding of mechanisms that generate robustness (invariance of function to non-trivial perturbations) in biological and social systems. It provides a classification of these mechanisms in pursuit of more general principles that confer robustness at different time and space scales. +
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Here are some references that explain how '''mortality plateaus''' arise.
Steinsaltz and Evans shows how mortality plateaus arise through convergence to a quasi-stationary distribution.
''Steinsaltz, D., and S. N. Evans. 2004. Markov mortality models: implications of quasistationarity and varying initial distributions. Theor. Popul. Biol. 65:319–337.''
Weitz and Fraser illustrate how such mortality plateaus arise from damage accumulation and purging of damage at the population level through a random walk with drift model.
''Weitz, J., and H. Fraser. 2001. Explaining mortality rate plateaus. Proc. Natl. Acad. Sci. USA 98:15383–15386.''
Mathematical similarities among Gamma Gompertz models and damage accumulation models (LeBras type models). We used this mathematical similarity in our Evolution paper for parameter estimation of the model.
''Yashin, A. I., J. W. Vaupel, and I. A. Iachine. 1994. A duality in aging: the equivalence of mortality models based on radically different concepts. Mech. Ageing Dev. 74:1–14.''
The Evolution paper that has most of the data that I presented including data on growth, division rates, cell elongation, size at division etc.
''Ulrich K Steiner, Adam Lenart, Ming Ni, Peipei Chen, Xiaohu Song, François Taddei, James W Vaupel, Ariel B Lindner. 2019.Two stochastic processes shape diverse senescence patterns in a single‐cell organism'"`UNIQ--nowiki-0000063D-QINU`"'https://doi.org/10.1111/evo.13708''
Here an '''asymmetric division model''' that has been inspired by the early e. coli aging work:
''Evans, S. N., and D. Steinsaltz. 2007. Damage segregation at fissioning may increase growth rates: a superprocessmodel. Theor. Popul.Biol. 71:473–490''
Quantifying mutation in singe e. coli cells by the mismatch repair system.
''Lydia Robert, Jean Ollion, Jerome Robert, Xiaohu Song, Ivan Matic, Marina Elez. [https://science.sciencemag.org/content/359/6381/1283.short Mutation dynamics and fitness effects followed in single cells].''
''Vol. 359, Issue 6381, pp. 1283-1286''
''DOI: 10.1126/science.aan0797''
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Here are some references to our work that I discussed which could be relevant: +
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Here is an excellent paper clearly spelling out all the hypotheses related to sleep for learning and forgetting (besides Susan and my J Neurosci 2019 articles!) - Jesse J. Langille, Remembering to forget: A dual role for sleep oscillations in memory consolidation and forgetting. Frontiers in Cellular Neuroscience 13:1, 2019. dob: 10.3389/fncel.2019.00071 +
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Here is the database on plant population dynamics I mentioned in my talk: www.compadre-db.org [https://www.compadre-db.org] The database is called the "COMPADRE Plant Matrix Model database".
There is a sister database called "COMADRE" for animals (same web address). +
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Population and the Environment: Analytical Demography and Applied Population Ethics/ChristopherCowie +
Hilary Greaves. Population Axiology. Philosophy Compass. (2017). A nice summary of some of the core issues in population axiology.
Mike Huemer. In Defense of Repugnance. Mind (2007). A more in-depth discussion of one of the controversial views in population axiology. +
Hunter, Luna and Norton (2015) offers a review of the sociological research on migration-environment linkages.
Riosmena, Nawrotzki and Hunter (2018) provides a recent example using census data of migration-environment research.
Black et al. (2011) provides an often-used framework for considering migration-environment linkages.
The collection of papers by Nawrotzki et al. offer a variety of examinations focus on Mexico-US migration as relate to climatic factors. +
I am currently using a very broad survey data from Indonesia, capturing very broad aspects of households. But the most interesting thing about this data is about children characteristics and migration characteristics from a couple of country such as Indonesia, Malaysia and Mexico. They called Family Life Survey. This survey data is freely available in RAND Corporation site. +
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I uploaded a paper by Alan Hastings and others on transient phenomena in ecology, published in Science as a review article.
I uploaded my paper on the stochastic lottery model that shows transitions between two different population states "What can Invasion Analyses Tell us about Evolution under Stochasticity in Finite Populations?". This paper develops an adaptive dynamics model for evolution of phenotype under a fecundity-survivorship trade off.
I posted a paper "[[Indirect genetic effects clarify how traits can evolve even when fitness does not]]" that relates to some of the discussion about interactions between individuals and the regulating factors that cause feedbacks and may themselves be evolving populations. +
I uploaded the three papers I presented in my talk:
Valdovinos et al (2013), Oikos: here I propose the model for the first time and use empirical networks.
Valdovinos et al (2016), Ecology Letters: Main results I presented in my talk. Niche partitioning via adaptive foraging reverses the effects of nesteness and connectance on species persistance in plant-pollinator networks.
Valdovinos et al (2018), Nature Communications: I used my model to generate a predictive framework on the invasion of alien pollinators and the subsequent effect on native species within plant-pollinator networks.
Brosi & Briggs (2013), PNAS: This is the data we used to test the prediction of my model on pollinators preferring specialist plants, when standardizing by plant and pollinator abundances. +
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I would suggest Indian Human Development Survey data for the researchers working on India. This is the only panel data set available for the country on a large scale. +
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In addition to bridging genetic and cellular to the cognitive and behavioral levels, an examination and integration of broader levels of complexity can further our understanding of when/why/how the brain breaks. I propose that this can be achieved by understanding how an individual’s lifestyle and environment relate to their resilience and vulnerability to brain decline.
I’m sharing a story (D. Buettner, NY Times, 2012) that begins to describe how multiple complex systems (including social, cultural, physiological, technological) may be important to consider for thinking about the health and robustness of an individual. I’m also sharing an article (Chan et al., PNAS, 2018) that summarizes my lab’s first attempt at integrating methods that examine an individual’s psycho-social environment with measures of their brain network organization to begin to understand the types of features that may lead to variability in brain network aging. +
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Keil et al. (2015) Nature Communications: derives the best math for calculating species loss under habitat loss.
Mendenhall et al. (2014) Nature: shows how human use of landscapes does not render them devoid of biodiversity and the consequences there of for conservation. +
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Paez et al. (2017) and Fleming-Davies et al. (2015) represent about 2/3's of the results that I presented in my talk. +
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Parkes 1992 "Fetal behavioural states: Sleep and wakefulness?" offers a review of the human and sheep fetal literature which casts doubt on whether this is any clear wakefulness in utero. (I was unable to add the reference to the 'reference' section).
Workman et al. 2013 (uploaded) is a nice approach to rigorously comparing development in species - which helps with thinking about ontogeny and phylogeny.
I thought of Baud et al. 2018 (uploaded) after talking to Alex about his research: I wonder if the seizures in this cohort unmasked a multi-day cycle which we all follow?
None of us mentioned the EEG 'cyclic alternating pattern' of sleep until I briefly raised on final day, but I like it as a framework which can encompass various specific graphoelements, e.g. K complexes, ripples, I have attached a paper by Halasz which interprets K complexes with reference to the cyclic alternating pattern. +
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Pillai & Jirsa 2017 argue that critical to our understanding of brain function is an appropriate representation of behavior, which then is to be placed in relation with brain network activity in space and time. Such representation must be based on dynamics (as opposed to derivatives thereof such as singular data features) and establishes the link between network structure and function. +
Podolsky et al find, In the context of regulatory networks and expression profiles, a connection between critical dynamics (the gene regulatory network is at the edge of stability) and aging. This link between criticality (often associated to "functionality" and flexibility) and aging is particularly intriguing also if translated into the context of neural networks and brain diseases. +
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Quantifying resilience of humans and other animals in PNAS 2018 is a review that covers ideas generated by a related workshop of animal scientists and medical researchers, focusing more on the level of networks of functions at the organ and organism level. +
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Related to the discussion of flexible distributed processing (that came up with Caterina's presentation) - there is a great paper showing how the neural code evolves despite stability in the bird song.
Liberti, W. A., Markowitz, J. E., Perkins, L. N., Liberti, D. C., Leman, D. P., Guitchounts, G., et al. (2016). Unstable neurons underlie a stable learned behavior. ''Nature Neuroscience'', ''19''(12), 1665–1671. '"`UNIQ--nowiki-00000550-QINU`"'
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127780/ +
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The Goldberger et al papers review of the Physionet resource and some of the types of analysis that we were performing in the lat 1990s. The Fukuda et al reviews a particular approach to deal with nonstationary time series.
The Amaral et al PNAS presents a simplified network model for generating complex dynamics.
The Amaral PNAS reviews the use cartographic representations of large, complex multilevel networks to summarize information on those network and gain insight.
The Goyal and Maslov paper proposes a model for explaining the structure and resilience of microbial communities. The Stouffer et al papers discusses models of food web structure. +
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The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition|The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression).
'''Access the Emory CASAS Cancer Survival Analysis Suite here: http://bbisr.shinyapps.winship.emory.edu/CASAS/''' +
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The volume by Howard Pattee entitled "Hierarchy Theory; The Challenge of Complex Systems.", which was published in 1973 could be very relevant to the work on multiscale resilience that Alfonse Hoekstra presented. +
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Venki Ramakrishnan's pre-meeting questions for consideration:
1. Evolutionary theories of aging:
All predict increase in mortality with age – something Vaupel and Baudisch and others have argued against. Others say that the apparent reduction of mortality or even a plateau is not because the animal has become more youthful or has stopped aging. Rather by growing larger, stronger or simply more experienced, it is better able to feed and protect itself. Some animals continue to grow for some time even after they start reproducing.
Another issue is the plasticity of lifespan, e.g. hunter-gatherers live much less than humans in modern societies. But the counter-argument is that this is not so different from animals protected in captivity generally living much longer than in the wild. One is removing extrinsic causes of death, which is not the same as preventing aging.
2. Elasticity of lifespan:
How extendable is lifespan? Clearly, there was a significant buffer because even though average lifespan was about 40 for most of our history, once extrinsic causes (safety, war etc) were removed, and even just diseases that caused infant mortality, lifespan expanded. Further expansion came when causes of adult mortality (cardiovascular disease, diabetes) were reduced or eliminated. Is there a limit and if so, what is it?
In other words can a living system be rejuvenated indefinitely? How similar is this to organizations, eg. companies or larger units? Or a mechanical device – eg replacing parts? Ultimately what is the limit imposed by chemistry and physics (including second law)?
3. Metabolic theory of aging (I believe I heard this from Geoffrey West):
Longevity inversely related to metabolic rate. One example was the number of heartbeats per lifetime being roughly the same for different species. Another is that long-lived species seemed to have very low metabolic rate (naked mole rat?). However, I believe there are exceptions to this. +
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Village Ecodynamics Project
<u>http://village.anth.wsu.edu/publications</u>
Relevance: paleoenvironmental reconstructions, identifying viable areas for food production in the past, archaeological work on different ways societies dealt with environmental changes
SKOPE (Synthesizing Knowledge of Past Environments)
<u>https://app.openskope.org/app/discover</u>
Relevance: paleoenvironmental data and models (including precipitation and growing degree days) available for open source use/download (note: the website is not fully functional yet, but hopefully will be soon)
Cajete, Gregory (ed.). 1999. <u>A People’s Ecology: Explorations in Sustainable Living.</u> Clear Light Publishers, Santa Fe.
Relevance: indigenous perspectives on the relationship of people and environment
Ingram, Scott E., and Robert C. Hunt (eds.). 2015. <u>Traditional Arid Lands Agriculture: Understanding the Past for the Future</u>. University of Arizona Press, Tucson.
- Especially the chapter called “Understanding the Agricultural Consequences of Aggregation”
Relevance: how populations adapt/innovate to increase the productivity/viability of landscapes with limiting resources (in this case, limited precipitation/irrigation)
Killion, Thomas (ed.). 1992. <u>Gardens of Prehistory: The Archaeology of Settlement Agriculture in Greater Mesoamerica.</u> The University of Alabama Press, Tuscaloosa.
Relevance: how agriculture changes/structures society and the environment (and vice versa) +
Population and the Environment: Analytical Demography and Applied Population Ethics/KaileyMartinez +
https://www.tdar.org
tDAR is a digital collection of records of international archaeological investigations. There are many types of studies available and it is constantly being updated with new publications. +
