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Life History Theory - Science topic

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It seems that they both study social influences on people's life, but they originate from different backgrounds(the former one is from social studies and the later one is from biology). Why researchers didn't combine them? Or is there any differences between them?
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Thematic analysis is more flexible since you can use different kinds of data such as interviews and journals (it allows interpretive method of data in your analysis). You don't really need a ground theory type of approach : )
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It is widely accepted that the major factor affecting the evolutionary optimization of animal life histories is energy balance, therefore studies focus on the energy costs and benefits of adaptations, the efficiency of energy acquisition and investment, and limits to energy budgets. However, at the very least in heterotrophs, equally important seems to be the problem of maintaining stoichiometric balance.
There are two approaches in eco-evo studies that consider the matter balance as complementary to the energy balance: ecological stoichiometry and nutritional geometry. However, in my opinion, such studies are limited and after 30 years after Tilman's and Reiners' works (below), still "energocentric" point of view dominates in ecology and evolution, that carelessly underrates the need to balance the diet also in terms of the matter (including the Law of Conservation of Mass).
This is only my point of view, possibly the wrong one. I would like to ask all of you: what is your opinion?
My question was introduced as briefly as possible, don't hesitate to dig deeper and extend it!
Below I present four important studies related to the topic, just to start with.
Kind regards,
Michał Filipiak
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Hi,
The question is how to integrate the stoichiometric concepts into the field data recalculations calculation ? Now field hydrobiologist can use coefficients of tempreture dependend functional indices like respiration or production, and amadements on oxygen concentrtation. But how to calculate disbalance or lack???
Andrey
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I am comparing the Von Bert life history parameters of three populations of a single shark species. In two populations I have negative values for the birth size estimate. One population (Western Cape) is biased towards older, mature individuals whilst the other (Eastern Cape) is biased towards small juveniles. The Eastern Cape population also shows an unrealistically large estimate of asymptotic length which I have found to be cause by small sample size and a bias towards small specimens. 
I am well aware of the fact that it is not possible to have a negative birth size. I cannot, however, find information as to why this might happen and if there is anything that can be done to correct these values. 
Can anyone shed some light on these unrealistic asymptotic length and size at birth estimates?
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T0 is the time at length=0, not the length at time=0.  This is a common misconception.  As such, it should always be negative, as fish have length at hatch (i.e. length at time=0 is positive).  If you truly mean that the length at time=0 is negative, then yes, you have a problem.  One solution is to add data for size at birth ( length at time = 0) using values from the literature and "fix" the size at time=0 to that size at birth.  This can be done in many modeling platforms by fixing the y intercept.
If you are only trying to use your growth curve for estimating age from size of small animals, I wouldn't worry about the poor fit to L infinity, as you will never extrapolate beyond the range of your data.  If this is a curve you intend to publish, then you will need more representative samples.
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I am comparing longevity using as a proxy simulations of maximum conditional lifespan based on published matrix models. Most of the transitions that I’m using are based on annual population changes (from 1 year to the next year), however, some other transition matrices are based on variable time intervals (months, 2-year, 5-year, etc… How should I standardize these values to make them comparable? An easy way I have in mind would be just to divide the estimated lifespan by 12 in the case of monthly matrices or multiply by 2 when dealing with lifespan calculated based on biannual matrices, but I am not sure if that would be appropriated. Any suggestion?
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I think that in the supplementary information of Jones et al. 2014 Nature, you may find the response. They have also R code for calculations of life expectancy. 
Good luck!
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Are long-lived species more resistant to extreme disturbances (i. e. heat waves, fires, hurricanes, etc) than shorter-lived species? If I’m right, longevity is driven by survival rates. Therefore, I expect long-lived species to be more resistant to external disturbances (at least disturbances that were frequent in the past), unfortunately I haven't found any empirical evidence of this pattern. I’d welcome any reference related to this topic. Thanks.
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A very interesting article by a fellow RG member that may perhaps provide some answers. Short-lived species may still thrive by virtue of higher reproductive rates kicking in at an earlier age. The survival of the species may be due to an insular environment that creates a lack of predation pressure (island effect), as was probably the case with the mammoths initially. But once the climatic changes brought about an overlap in territories, the insufficient time for metabolic rates to adapt and the increased predation pressure brought about a quick extinction.
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I am interested in an aspect of life history theory, but I can't find any previous literature on the subject. How do long-lived organisms make decisions about resource use? For example, since survivorship is high, they could put a burst of resources into repro a little at a time over many years, or many other possibilities.
For example, a turtle that comes across a carcass, or a tree root that happens into a large pocket of high nutrient soil. Short-lived organisms might pop that sort of thing right into reproduction or growth right away, but long-lived organisms have many more options. Any papers on this?
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Russell, I think your question has some relation to plant growth strategy. Many plants will not develop generative organs until their roots will occupy all possible volume of soil.
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There are some measures for slow LHS (alhb, mini-k, hkss), but I could not find a scale for measuring fast life history strategy in humans. Does such a scale exist? Will it make sense to develop such a scale (since I don't think that fast LHS could be accurately measured with K-strategy scales, for example by just inverting it)? Thanks in advance for your hints!
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The lack of an r-scale has been a sore spot for some in our research group for some time. I know of no such measure. I would also be careful of mixing proximate psychological processes with less proximate LH outcomes. The Copping et al. (2014) criticism, makes a valid point regarding the relationship between psychometric measures and biometric indicators (I think that the outright dismissal of self-report measures is simply wrong, but I can see how one can have that approach ).
I know that AJ's preferred method is using the MiniK, TIPI, SF36, and HKSS to create a "SuperK" factor, but that doesn't address your question. Unfortunately, most of the research on r/K is done on college student or volunteers, which will tend toward being slower almost by definition. 
I'm kind of rambling, so I'll stop. I do think the question is a great one and someone should definitely get on it (benchmarking it against the K measures).
 As an aside, you can find a recent meta analysis of the Mini-K in the attached link. 
 Raf