Property:Reference material notes
From Complex Time
This is a property of type Text.
P
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. +
I
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 +
C
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. +
A
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''
C
Here are some references to our work that I discussed which could be relevant: +
W
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 +
A
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). +
P
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. +
I
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. +
C
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. +
I
Paez et al. (2017) and Fleming-Davies et al. (2015) represent about 2/3's of the results that I presented in my talk. +
W
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. +
C
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. +
