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Population Dynamics - Science topic

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Population dynamics is usually linked to system stability. For example, over population is linked to system unsustainability, and possible system collapse through overshooting behavior like ecological overshoot. Population dynamics is rarely linked to market pricing structures as markets are usually presented as supply and demand interactions consistent with their price structures. But market price structures can be seen as linked to the nature of the population they serve. Hence, population dynamics appears to be the connection between market price structure and system stability.
And this raises the question, Is population dynamics the link between market pricing and system stability? I think yes, what do you think?
Please, feel free to share your comments, Yes and why you think is Yes or No, and why you think is No.
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Dariusz, the way you try to share your material is not helpful for sharing ideas ...you say Yes, In will leave it there.
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I am wondering how one determines the appropriate size of a Leslie Matrix. This is one of the first decisions made in constructing a Leslie matrix model and it determines the number of age groups, the age class width, and the projection interval. Is bigger always better? In other words, is it always better to have more age classes if the data allow?
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It would seem the finer-grained the Age Class structure, the more precise the output -- provided the inputs for each Age Class are accurate, e.g. Population, Birth Rate, Survival Rate. Also, of course, the Age Classes must encompass the life span of the species one is modeling, with the highest Age Class being the only Class with Survival Rate 0.
To model a life span of, say, 10 years one might employ 10 Age Classes, if there is input data to support it.
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We hear about environmental problems or social problems or socio-environmental problems associated with business as usual, problems being exacerbated currently by over population pressures and overshooting pressures. Hence, all those problems and pressures seem to be associated with non-optimal market conditions in practice, but conditions that are assumed to be optimal in theory, hinting towards a practice-theory inconsistency problem.
And this raises the question, Is the destruction of full optimality at the heart of system unsustainability problems? I think yes, what do you think?
Note: Moving away from full optimality thinking is what is meant here when saying "the destruction of full optimality".
Please, feel free to express your own views on the question, Yes, and why you think so? No, and why you think so?
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Dear Lucio
What I tried to say is that, in my view it is characteristic to the sustainability-related discussion in economics to disregard the factor time when talking about pricing and equillibrium.
Regards
Michael
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I have 3 large household cross-section data sets each 13 years apart, starting 1992-93. I want to test the stability of coefficients across the 3 points estimated from structural reduced form single equation demographic transition model.
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Stability is a concept that largely associated with time series data. There are almost 161 different definitions of stability. In mathematics one can use Lyapunov Experiment as stability measure.
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According to the census 2011 uttarakhand had 1048 uninhabited village. in 2017, 734 more villages added in the list. According to some resources and agencies there are more than 800 villages which have the decreasing population more than 50% from 2017 to 2021.
i am working on my PG thesis on above topic. any lead for the same will be appreciated and much helpful for my report.
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Please take a look at this useful RG link.
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I plan to estimate the probability that the Ussuri dhole (Cuon alpinus alpinus) is extant in Russia and test the null hypothesis that extinction has not occurred. Based on several analytical papers and reviews (Solow, 2005; Rivadeneira et al., 2009; Lee et al., 2013; Clements et al., 2013; Boakes et al., 2015) I choose the Bayesian approach (Solow, 1993) and Optimal Linear Estimation (OLE) (Roberts and Solow, 2003).
I decided to use 'sExtinct' R-package (Clements, 2013) for OLE calculation.
Firstly, I tested the package on the sighting record of the Caribbean monk seal (Solow, 2005) and the Dodo (Roberts and Solow, 2003) (I attached a file with my script). Surprisingly, but the output of calculation in the package (lowerCI and upperCI) is discordant with the corresponding estimation in the original papers (Solow, 2005; Roberts and Solow, 2003).
For example, according to Solow's estimation (2005), the upper bound of the 0.95 confidence interval for the Caribbean monk seal is 2028. The 'sExtinction' package estimated the upper bound as 2093.799.
I received a similar result for Dodo: Roberts and Solow estimated the upper bound (95%) as 1797; the 'sExtinction' package gave out 1834.568.
I am at a loss. Where is the bug?
So, there is the question:
Can I use the 'sExtinction' package or I should write my own code by the description in original papers?
I invite @Christopher_Clements, @T_Lee2, @Marcelo_Rivadeneira, @Simon_Blomberg, @Diana_Fisher, and everyone for discussion.
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Thank you, Caleb A. Aldridge !
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I highly appreciate if the suggested topic relates on the application of graph theory in environmental science, particularly on the population/competition dynamics.
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This is probably too simple, but I will never get round to adapting Daisyworld (https://en.wikipedia.org/wiki/Daisyworld and references their in) to model Arctic sea-ice extent. Daisyworld consists of black an white daisies. Arctic sea ice consists of white ice and black melt ponds and polynas. As solar energy increases the temperature in Daisyworld remains constant until it passes a tipping point and temperature jumps. As CO2 increases the Arctic sea ice extent has remained fairly constant, but is there a tipping value of CO2 where the sea ice will disappear?
if you do go ahead with this, or want more info. please let me know.
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I have a continuous dataset of 90 species abundances, over 45 years and I am looking for a way to assess changes in the structure of their community over time. I have previously used bray-Curtis dissimilarity methods that appear to work well however I cannot seem to find a test to analysis the significance of the result. Previous suggestions 
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Dear Dr. Stephanie Allen ,
It's almost impossible to give an accurate answer without having the data in front of you, according to what you said it seems that an ANOVA test could fit your expectations, so I agree with Dr. Mohammed Abdullah Dakhil and Dr. Abhijit Mitra . Good luck.
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As length data is used to estimate different population dynamics parameters, I want to know what parameters are obtained using the age data and how the data of age of the fish needs to be framed to get accurate results.
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In a Next Generation Matrix, the dominant eigenvalue (or spectral radius) corresponds to R0 (Diekmann, O., Heesterbeek, J. A., & Roberts, M. G., 2010).
What information then carry the remaining (non-dominant) eigenvalues of this matrix? Can they inform us about other dynamics of an epidemic?
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A population decline (or depopulation) in humans is a reduction in a human population caused by events such as long-term demographic trends, as in sub-replacement fertility, urban decay due to violence, disease, or other catastrophes. According to a controversial theory: shrink and prosper, the accompanying benefits of depopulation could be identified after the post-Civil War Gilded Age, post-World War I economic boom, and the post-World War II economic boom.
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Dear Dr. László Vértesy , thank you for the thought provoking question.
Advantages of a decreasing population
Long term benefits
1. Will bring better balance among the living beings/organisms.
2. Extinction of Religions and Gods
3. People will celebrate life. I think there will be no artificial, man-made death in the name of God(s), Religion, Nationality etc.,
4. People will die only by natural causes. The reaming few (People) will support each other to save the life other every other person.
Short term benefits
1. Less pollution, Lesser need for food, land and other resources.
2. More pet animals are saved from the harassment of humankind and live in their natural habitat. They may live happily (may be unhappily at times) with their Father, Mother, Sisters, Brothers and could escape from the confinement.
Disadvantages of a decreasing population
1. Long term impact - Extinction of Languages, Culture, Food Choices
2. Short term impact - Economy will shrink, more unemployment, population will have fewer young people supporting huge number of elders.
3. The streets, seemingly empty streets, buildings etc., may look devilish.
Some useful links:
Thank you for this wonderful opportunity to share my views.
Best wishes and regards
Yoganandan
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I have come across some Nonlinear Control System problems where I need to implement Matlab code but unfortunately, it's getting too tough to start with. I can some of the problems such as,
1. Population Dynamics using Lotka–Volterra differential equations.
2. Continuous Stirred Tank Reactor (CSTR) using van–der–Vusse
reaction scheme.
3. PI Controller for Linear and Nonlinear system
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A total of 50 individuals of the species were collected monthly from sampling area for 13 months to study population dynamics of this bivalve. I have always understood that we need at least 30 to be able to reasonably expect a statistical analysis, but I heard lately that it was not enough! what do you think about this please? Is there a specific condition for determining the sample size?
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Sample Size Rule
Sekaran (2013) wrote:
"Roscoe (1975) proposes the following rules of thumb for determining sample size:
1. Sample sizes larger than 30 and less than 500 are appropriate for most research.
2. Where samples are to be broken into sub-samples;(male/females, juniors/seniors, etc.), a minimum sample size of 30 for each category is necessary.
3. In multivariate research (including multiple regression analyses),the sample size should be several times (preferably 10 times or more) as large as the number of variables in the study.
4. For simple experimental research with tight experimental controls (matched pairs, etc.), successful research is possible with samples as small as 10 to 20 in size."
Reference
Sekaran, U., 2003. Research methods for business: A skill building approach. John Wiley & Sons.
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I am interested in identifying sex-specific survival rates. I have a long-term dataset of individually marked birds from one breeding colony. Of these, I am able to genetically sex a subset. Additionally, I have access to mark-recapture data on nearby breeding colonies and so can estimate emigration from my target breeding colony. However, I am only able to sex individuals from my focal breeding colony.
My research project addresses both survival and dispersal (permanent emigration). So I'd like to disentangle these as much as possible and look at effects of sex. One approach is to only model the subset of birds I'm able to sex. Another approach would be to model all individuals, including unknown sex, and apply the techniques in Nichols et al. (2004) to apply probabilities of "male or female" to unknown-sex birds (I may be summarizing that poorly).
Can I use a multi-site model if I only include individuals marked on one colony, but do have data for permanent emigration to other colonies? Would it make sense to use a multi-site model with all individuals from all colonies but only have sex information from one colony?
Thanks in advance!
Nichols, J.D., Kendall, W.L., Hines, J.E. and Spendelow, J.A. 2004. Estimation of sex‐specific survival from capture–recapture data when sex is not always known. Ecology 85(12):3192-3201.
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May be you must used a multi variate analysis bu using your data about breeding colony and inserted nearby breeding colony as supplementary data.
Also, if you can estimate migration between colony, you can added this parameter as factor to your analysis.
Good luck
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Fluctuations in population numbers, abundance, or density from one time step to the next are the norm. Population cycles make up a special type of population fluctuation, and the growth curves in population cycles are marked by distinct amplitudes and periods that set them apart from other population fluctuations. In the animal kingdom, it may be explained that this fluctuation is the result of a change in the food chain and the density of consumers' arrangement, which over the years entails increases and decreases in the size of the population. But the situation is different in the plant kingdom: the plant may be lost and disappear for a long time in one region, and then it will return strongly and disappear in another region and so on. Some may explain this by the presence of seeds in the soil, which allow the plant to return from the dark. The reason for its disappearance may be natural as a result of climate change or the destruction of environments and others. Generally, this fluctuation is normal, but when is the matter dangerous, worrying and warranting intervention? How can we expect that the plant will return after its disappearance in a region?
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I strongly agree with you that tracking the population size at close intervals and for a long period of time may give an indication of the condition expected in the future. Acctually I find this is very difficult, especially in endemic species of little geographic range, which have small numbers of mature individuals.
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I am working on a paper that looks into the dynamics of the spread of Wolbachia and its potential impact on dengue transmission, particularly in the Philippines.
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Interesting research, although not in my field of work. I have heard about Wolbachia infections in other Diptera possibly causing melanistic non-fertile females. Is this the main reason for the slowing of the spread of Dengue? Is there any research on how Wolbachia will affect other insects?
Best wishes,
Jeroen
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Are there any cyclical animal populations in the tropic?
I've been writing an evolutionary biology hypothesis, that claims that cyclical animal populations are actually a population-wide multi-year hormone cycles; not that different from menstrual cycles: https://www.researchgate.net/project/80-year-generational-hypothalamic-hormone-cycle-spanning-across-4-generations
The current assumption is that the annual changes in daylight enables the "counting" of years for biology, but since the amount of daylight is quite stable in the tropic, the "counting" of years wouldn't be possible there. This is why I'm asking: are there any cyclical animal populations in the tropic? The requirements are that the cycle is multi-year and has similar characteristics compared to the other cyclical populations (of lemmings, voles, hares, etc.)
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Of couse, there is cyclical animal population variantoin in hole planet due periodic elleven years variation in solar irradiation
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In a population dynamical system, the non-explosion property is often not good enough but the property of ultimate boundedness is more desired. The conditions for the ultimate boundedness are much more complicated than the conditions for the non-explosion.
The nonexplosion property in a population dynamical system is often not good enough but the property of ultimate boundedness is more desired
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What is the effect of increasing or decreasing population size and the number of iterations on the quality of solutions and the computational effort required by the Swarm Intelligence algorithms?
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The increase of the population leads to (at least initially) increased diversity of the population, but if one increases the population size too much, there may be slow convergence of the population to the global optimum. But if the population is too small, it will lead to entrapment to local optima. It is widely suggested to increase the population in order to avoid local optima, especially if the objective function has many parameters. As for the number of iterations, an increased number will increase the possibility for convergence to global optimum, but you may start doing useless computations. In this case, you might need a good termination criterion that prevents useless computations after reaching global optimum.
For the latter, read the following paper.
Spanakis, C., Mathioudakis, E., Kampanis, N., Tsiknakis, M., & Marias, K. (2016). A Proposed Method for Improving Rigid Registration Robustness. International Journal of Computer Science and Information Security, 14(5), 1.
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Kite diagramme that i am expecting to draw is population dynamics of animals in a national park Sri Lanka over period from Jan 2018 to Dec 2019.
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R Statistical Software is available for free online: http://cran.r-project.org
Here is also information on how to make a kite diagram in R: https://rpubs.com/thoughtfulbloke/kitegraph
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I am currently pursuing my Ph.D. in fisheries, Can you assist me with knowing how to apply R in fish modeling as this would help me in analyzing my data. If there is any opportunity to use your lab too I will be very grateful.
Thanks
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I second @Thierry. The site should help you with all you need
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Codes in other programs (python) will also be helpful.
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It is what i want!
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Climate Change & ENSO effects on weed population dynamics.
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Hola Guillermo
Te recomiendo algunos de mis artículos donde utilizo una metodología para separar el efecto de la regulación poblacional de factores climáticos globales,
abrazo
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I am studying Lake Tana Fisheries. Some species currently found under the IUCN list.
Some driving forces are irrigation and sand mining on their spawning grounds, overfishing, drought, waste from agriculture and urbanization, invasive species like water hyacinth. So, recently, some species are critically treated. I want to know the whole the species population structure from the lake, their status from their spawning grounds (tributary rivers), population dynamics, the extent of impact levels due to the above driving forces, population genetics from lake and rivers.
So, I am happy any one help me which type of models, methodology and share me your experience or any recent related works
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My opinion, in general terms:
1) You'll want to figure out how to quantify the anthropogenic factors you are interested in (i.e., overfishing, agricultural use, etc.) so you can evaluate them as potential explanatory variables. For the tributary rivers (if that is your scale of interest), you could quantify agricultural or urban land use by calculating the % of the catchment or riparian area bordering the tributary that consists of agricultural or urban land. To quantify irrigation effects, you could use the quantity of water abstracted (if that is available), percent water loss, or # of abstraction points. Invasive species: density per unit area of non-native taxa, or non-native to native taxa abundance ratio. Sand mining: number of mines or relative amount of sand extracted per tributary. Overfishing: average annual harvest (by weight or number) for each tributary.
2) You'll need to identify one or more response metrics such as fish density, fish average size, number of spawning pairs, average body condition, size spectra, or food-chain length that you believe will be affected by the habitat factors. Then you'll need to acquire data (historical or go to the field) to collect these metrics.
3) Test your hypotheses using a modelling framework suitable for your data.
You might find the papers I've attached useful: 1) we focused on the effects of water abstraction on fish assemblages and 2) the effects of catchment land cover on fish assemblages
I am less-versed in the genetics realm so I won't weigh in on what the approach should be for the population genetics aspect.
Hope that helps and good luck!
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In many publications on the topic of modelling residential mobility I read, agents are regarded as households and can only make decisions (here relocating) as one single entity. The question whether an agent with given characteristics has a propensity for changing housing or not is often answered for an entire household. Restricting households to only be able to stay or move as a whole seems not appropriate. An alternative would be to compute/model on the individual level and take the household type into consideration. However, individuals as part of a household should still be able to move together which would be rather unlikely in this case. How is this taking care of in research?
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It's been a while, but I always modelled as households. One should strictly allow for household formation behaviour eg children leaving home and forming new households, divorce, group households splitting and re-assembling. Depending on what you are trying to do I would not expect the propensity to move to vary too much.
If you are doing logit/probit modelling - it should not be too hard to add in propensity to split, followed by propensity to move, in a hierarchical framework.
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Hello.
I'm performing a length-weight relationship in two populations of caridean shrimps (they are from two distinct seasonal periods), the pooled data was divided into males, females, non-ovigerous females and ovigerous females. I performed the equation and obtained the b slopes of each sub-sample analyzed, but now I want to compare those slopes to know if there is any different between them. I read that I could do an ANCOVA to compare, but I'm not sure if there is another methodology. Thank you.
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You could indeed use ANCOVA/linear regression (after checking your model assumptions, i.e. linear relationship between length and weight (in model 1), normality and homoscedasticity of residuals and independence of observations).
As observations from the same population are probably not independent, you should add population as a variable in your model.
There are two possibilities:
Model 1: model the relationship between length and weight, including an interaction with sex.
Since you are interested in the difference of length-weight slopes between sexes, you should add an interaction effect in your model.
The model looks something like this:
length ~ weight + sex + population + weight*sex
If the interaction effect is significant, the length/weight slope differs with sex. To identify the which of the four groups differ significantly from each other, you can carry out a post-hoc test on the interaction term (e.g. Tukey test). If the interaction is not significant, you cannot assume that the l/w ratio is significantly different between any of the four groups.
Model 2: use length/weight ratio as a response variable
Another way would be to use the length/weight relationship directly in a model:
length/weight ~ Sex + Population
Here, we do not need an interaction term, as the relation between the l/w ratio and the four ‘Sex’-groups is implicitly part of the model. If ‘sex’ is significant, you can again perform a post-hoc test on ‘sex’.
If you are using R, this link might be helpful: https://rcompanion.org/handbook/G_09.html
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I was able to set the scenario (code provided) but bit unsatisfied. I would like ghost population to reach near 0 or 1 (time scale) as I am not sure whether unsampled population exists today. How to set the code so that ghost population appears distinct in the scenario picture and reaches to current time scale?
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Hi Gerlien, I had 5 populations (sampled) and a population was assumed as "GHOST".
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I have observed that the population dynamics models are having non-negativeness, boundedness and uniqueness as their essential part and it is mentioned in the literature many times, but I do not know the reason behind it. Please put light on it.
Thank You in advance.
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In many population dynamic models bounded estimates are essential given the nature of the parameters (i.e. survival or occupancy). For example, survival is a probability which is bounded because it is not possible to have more than 100% survival. In turn it is not possible to have less than 0% survival. However, some parameters such as recruitment or population growth rate are not bounded, because they are rates.
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I have done morphology, population dynamics and Phylogenetic analysis of some cockroach nematodes. How to do advance work in that. Please suggest.
Thanks
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You can go through the cited references for ideas.
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I am doing a bioinformatics analysis. According to I red. Populations
at demographic equilibrium show a multimodal distribution. Unimodal mismatch
distributions have been interpreted as being due to past demographic expansions, but how do they look like?, Could anyone help me, please :)?
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I have got a mistmatch distribution like this. SSD value is insignificant. How can i interpret this data?
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A two year insect population dynamics data shows slight variation in their performances w.r.t meteorological parameters. Data pooling will be helpful for me?
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Depends....common polling techniques occur over time and place, and they are useful for increasing the number of observation when the N is low.
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I am seeking suggestions on some field methods for analyzing Population Viability Analysis (PVA).
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The free software Vortex (http://www.vortex10.org/Vortex10.aspx) is quite handy for your analyse if you have the main bioparameters.
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Is there any paper on how floaters develop 'territoriality' between them in birds? As they are not territorial individuals per se, I wondering if there is some background on how they exploit resources on different ways, being some of them more associated with high-quality resources whereas others not, mainly due to agonistic behavior.
Thanks!
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Hey there,
I've attached a couple papers that hopefully address your questions. For general background in multiple taxa, here's one:
Overall, Corvids are excellent models in studies concerning non-breeding individuals as floaters and the benefits of cooperative breeding strategies, and multiple papers have addressed how non-breeding individuals have 'taken over' previously established territories. In Gayou (1986), its discussed how Green Jay floaters additionally engage in territory defense as part of a non-breeding cooperative family unit. In researching this, I found another excellent study concerning Purple Gallinules and how they change status, from floater to territoriality and back to floater status given habitat availability. I hope these help!
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I am searching for ways to promote lizard and amphibian capacities as a profit from the restauration of their habitat in private gardens.
Therefore I need evidence of these actions
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Second picture shows the mosaic highly found in our project of okra under the varietal screening under the different control practices. But difficult to say what may be this?? 
Similarly the first picture shows the reddening on the vein on under surface of leaves. This case was found even after the spray of different pesticides i.e. botanicals to chemicals. The pest population dynamics was high with majority of nymph of jassids and whitefly too.
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Hi,
I encounter this species every spring while completing surveys around the NSW Central Coast. This species is listed as vulnerable under both the NSW Biodiversity Conservation Act and the Commonwealth EPBC Act.
Its a beautiful little plant, but I often hear botanists complain that this species is too common to be considered rare, and therefore should be delisted from each of the acts.
I'm interested in what other botanists think about this. Particularly, those with knowledge of plant population dynamics and evolution.
cheers,
Gilbert
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Dear all,
I have gathered samples of Dreissenid mussels at different locations in a lake. After counting and measuring the mussels, histograms featuring the number of individuals per shell length ranging from ≤0.4cm to 3.5cm have been created, most of which are showing multimodal distributions but some are unimodal, too.
What is being tried to do, apart from visually determining the size-frequency distributions, is to apply a statistical mathematical tool in R that goes through all of these data and attempts to classify recurring groups that then ideally represent age-groups (cohorts, populations) of the mussels.
It seems like packages in R that might be helpful are 'mclust' and 'Rmixmod' both of which have already been tried. However, in the end, always a dead-end has been reached so it got me wondering whether the arrangement of my data may be the problem or there is another underlying cause.
Has anyone already encountered and maybe solved equal problems and might possibly be up to having a look into the organisation of my size-frequency data?
Similar methods to what I am trying to do have been exercised for example in:
Comtet, Desbruyeres (1998) Population structure and recruitment in mytilid bivalves from the Lucky Strike and Menez Gwen hydrothermal vent fields (37'17'N and 37"501N on the Mid-Atlantic Ridge)
and
Taylor et al (2009) Using length-frequency data to elucidate the population dynamics of Argulus foliaceus (Crustacea: Branchiura)
Thanks to everyone wanting to help!
Benjamin Wegner
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Hi, you can have a look at the R library "TropFishR" in R-cran (https://cran.r-project.org/web/packages/TropFishR) or in github (https://github.com/tokami/TropFishR). The Bhattacharya’s method can suit your problem.
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Hello ResearchGate community,
Our lab has recently adopted GBS SNP data to gain a better insight into population dynamics of endangered non-model species in Australia (mainly amphibians, geckos and marsupials).
For most projects, after gaining a first insight into current population structure, we would like to understand whether the observed structure is a result of recent or ancient splits. As most of us are relatively new to the world of GBS and SNP data, we would really appreciate if you could point us to the best methodologies for doing so.
Any help is much appreciated.
Kind regards,
Lorenzo
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I am by no means an expert in this but will share what I have come across. I believe BEAST, in particular the extension SNAPP, provides a way to generate phylogenies using allele frequencies. Then you can fix divergence dates on some of your nodes and estimate them for the rest of the tree. BEAST* also offers an option that exploit the actual haplotypes (not just SNPs) to estimate divergence. See Saglam et al. ( ) for an example.
I also received some advice to use MCMCtree to do something similar, which is part of the PAML package. Good luck.
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Hi,
I would like to derive a mathematical model that can explain the change in frequencies of two genes that cause resistance to a particular antibiotic. Based on the literature, one of the genes is expressed only constitutive, while the other is a membrane protein. I would like to know the methods and protocols to follow for using evolutionary game theory. As I am learning it right now, any reference to similar articles or texts would be much appreciated.
Thanks.
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Mcelreath and Boyd's social evolution is a simple yet comprehensive start to help guide you in modeling these evolutionary processes using game theory (http://press.uchicago.edu/ucp/books/book/chicago/M/bo4343149.html).
Gintis's book "Game Theory Evolving" (https://press.princeton.edu/titles/8900.html) is a slightly more advanced but also easily accessible book. The topic is covered in two chapters on evolutionary dynamics.
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Turing spoke of population dynamics. I would start with a cellular structure with 12 connections. Move on to development of awareness, Self, Other and Environment using multi-sensory inputs and a sematosensory cortex. You will need to dynamically encode, store, sort and delete memory. Your internal clock will be points, not intervals as explained by Oliver Sacks in the river of consciousness. Your synthetic must have time to incubate in a safe environment. Sacks was wrong about salience, it is dynamically selected. But emotion in evolution is generated by the dance of the sympathic and parasympathic nervous systems, not the CNS. Hope this helps.
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How data will be collected?
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Dear Dr. Oum, thank you so much for these documents. I will immediately study them.
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For population dynamics studies
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Can you be more precise on the species and the time frame you are working on? For field application (vs. for example the carrying capacity of an agar plate) you can model the different variables to which the species is linked (food, shelter, habitat...) and other life history variables such as mating and death rate and import it into HexSim. Another option, if you know a lot about the life history of the species, is to use Vortex. You can input the data in a way that will show you the resulting expected population size. The learning curve is much steeper with Vortex, but HexSim can bring you further.
If there is one limiting variable with a stronger impact than the others, then you can also calculate the number of individuals that can live out of the focal variable, but this is working for very specific situations only and I would not necessarily recommend it.
Good luck!
Amael
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mathematical biologist, population dynamics researcher
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Hi Jayaprakash,
NetLogo is a functional mathematical modeling system for many types of models, one of which is predator-prey relationships. Please see:
If you download NetLogo and run the model it provides some nice graphs. You can also adjust the model as you see fit quite easily.
Have fun with it. It is a useful program in spite of using the older Java technology :)
Regards,
Dave
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In the last 117 years the population of the US has only once declined against a steady background of population growth.  The world was hit by a massive influenza pandemic during that period but could that alone have accounted for this anomaly or was there another factor operating
July 1, 1919        104,514,000          1,306,000              1.26 
July 1, 1918        103,208,000            -60,000               -0.06 
July 1, 1917        103,268,000          1,307,000              1.27
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Barry,
I think that the July 1, 1918 U.S. population number is unreliable. Prior to 1940 the U.S. Census Bureau did not count U.S. citizens that were stationed overseas in the military. Since over 4 million U.S. soldiers were mobilized between August of 1917 and the summer of 1918 the U.S. Census Bureau had to estimate how many were overseas versus living but not counted in the States. I'd say that the margin of error probably covers that supposed decrease.
There also could have been a decrease in births in due to an immediate anticipation of U.S. involvement in the war. While 1917 looks like a negative growth, there was a substantial decrease in population growth during the Great Depression. It was only after the economy crossed the threshold of where it was at the time of the start of the depression in 1929 that the population continued its more regular trend. Interestingly, from an historic perspective, there was an increase in the U.S. birth rate in 1940 that continued through World War II. This increase in U.S. population growth probably consisted of wartime babies in families that were recovering from the Great Depression and had elected not to have children due to the economic conditions at the time.
JAG
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Boundedness of discrete dynamical systems.
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hi see this attach about this model
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Hello all,
I want to analyze the role of spatial vs environmental effect (through variation partitioning) on Notonecta species distribution among fishless ponds.  I have been using adespatial package to do that.
After calculating the MEMs, I need to estimate the Moran`s I spatial autocorrelation values, in addition, to the positive and negative part of this index for the ten MEMs I have.
In adespatial package, in the dbMEM example of mite data, Moran`s I is calculated using moran.randtest, without using the listw function as follows:
test<-moran.randtest(mite.dbmem1, nrepet=99)
However, in the explanation of the test itself, listw is been used:
moran.randtest(x, listw, nrepet = 999, ...)
I have analyzed my data with and without using the listw and I get different results. I was not sure which one of the two methods is more appropriate since both examples are given in the same package. I was wondering if anyone has used the function moran.randtest and if yes, do I need to use listw to calculate the Moran`s I index?
I would really appreciate your kind guidance!
Mitra
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Hello Sacha,
I have copied the analyses I have done to calculate spatial correlation here. I think as you mentioned in your earlier comment, my x (notonecta.dbmem1 has a listw attribute), so even if I do not type it in moran.randtest, listw will be used to calculate spatial autocorrelation here: 
> notonecta.dbmem1 <- dbmem(pond_coord, thresh=0.01707804, MEM.autocor = "non-null", store.listw = TRUE, silent = FALSE)
> attributes(notonecta.dbmem1)$values
[1] 0.14567211 0.04095702 -0.03654004 -0.09026366 -0.09084915 -0.09090909 -0.09090909
[8] -0.09090915 -0.14911688 -0.18256433
> attributes(notonecta.dbmem1)$listw
Characteristics of weights list object:
Neighbour list object:
Number of regions: 11
Number of nonzero links: 78
Percentage nonzero weights: 64.46281
Average number of links: 7.090909
Weights style: B
Weights constants summary:
n nn S0 S1 S2
B 11 121 76.8873 151.6504 2420.918
> test <- moran.randtest(notonecta.dbmem1, nrepet = 99)
> test
class: krandtest lightkrandtest
Monte-Carlo tests
Call: moran.randtest(x = notonecta.dbmem1, nrepet = 99)
Number of tests: 10
Adjustment method for multiple comparisons: none
Permutation number: 99
Test Obs Std.Obs Alter Pvalue
1 MEM1 0.22924883 5.0022965 greater 0.01
2 MEM2 0.06445536 2.9170811 greater 0.02
3 MEM3 -0.05750422 0.8315211 greater 0.19
4 MEM4 -0.14205079 -0.6149102 greater 0.68
5 MEM5 -0.14297220 -0.7215775 greater 0.74
6 MEM6 -0.14306653 -0.7440757 greater 0.77
7 MEM7 -0.14306653 -0.6722448 greater 0.80
8 MEM8 -0.14306663 -0.8240201 greater 0.82
9 MEM9 -0.23466999 -2.6063308 greater 0.99
10 MEM10 -0.28730731 -3.0581972 greater 1.00
Best,
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Hi all,
I studied Monochamus saltuarius, a major insect vector of pine wood nematode, in Korean White Pine forests using Mark-Release-Recapture based on pheromone traps. 
However, their recapture rate is very low about 2~3% during study periods, i.e., 4~6 individuals were only recaptured (total marked and released individuals were about 168~243 individuals).
Thus, in each session, only 1 beetle or no beetles were recaptured (My experiments were conducted 11 occasions).
In this case, can I analyze population size of M. saltuarius using estimation methods, such as Jolly-Seber model?
Thanks.
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The assumptions of the JS model are:
1. Every individual has the same probability of being caught in the sample, whether it is marked or unmarked.
2. Every marked individual has the same probability of surviving from t to the (t +1) sample.
3. Individuals do not lose their marks, and marks are not overlooked at capture.
4. Sampling time is negligible in relation to the intervals between samples.
Capture rate is not included, so you can use the model imo :)
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I have some individual interest to understand a little bit more these two phenomena, because the apparent mathematical similarities between them, and because some particularities, too. The overview to direct possible concerned researchers to solve this question can be seen in the attached link. Thanks for you interest.
Deleted research item The research item mentioned here has been deleted
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This is better explained here
Deleted research item The research item mentioned here has been deleted
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My Project concerns carabids morphometry and we have no samples of our model species from your region.
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My interest for Carabidae is to find small sized Mediterranean species which live on bushes, trees or in soil leaf litter. After being identified at species level, my interest is to find their preys. Particularly scale-insects, whiteflies, aphids, psyllids, (may be one day I will discover that they pey larvae of Cicadelloidea or Fulgoroidea), caterpillars, fly larvae, or Psocoidea. You know that dryness of summers is increasing in South France, from one year to another.
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the lowest risk methods with the best results. I've tried searching for literature on this but nothing came up. Anyone please help me.
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We're using the methods that our department is used to..active searching. Capture-mark-recapture.
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I'm doing a master on moose populations estimations with a citizen science approch.
I want to simulate a hunter and a moose population (of known size) in the same landscape, "make them meet", and see how the abundance estimations are biaised depending of the proportion of "cheaters" (hunters who don't say the right number of moose seen).
I explored the SELES and RAMAS/GIS softwares, but I'm not sure they are appropriate tools to simulate two animal populations movements in a static landscape.
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InsightMaker would work. It is very easy to use.
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This is to provide insight into whether or not human settlement density is affected by latitudinal position. 
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Please check thee useful PDF attachments.
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Climate refugia have been presented as possible mechanism allowing species to survive rapid climate shifts (e.g. Keppel et al. 2012, 2015; Franklin et al. 2014; Barrows and Fisher 2014; Barrows et al. 2016).  Modeling where climate refugia might occur is straightforward; zones of overlap between current distribution models and climate-shifted models could be such refugia. My question relates to both validating such models, and more to the point what are the population metrics that might characterize a population residing in a climate refugia? Here in California we have (possibly) emerged from a 5-year drought that may have been a window into how populations behave in response to climate change-like conditions. We are tracking communities/populations of lizards across a broad elevation gradient. None went extinct; all declined during the worst of the drought but then behaved differently along this gradient as the drought became less severe. 
1. some have remained at drought-level densities even after near-average rainfall returned.
2. some population densities increased linearly and others exponentially with increasing rainfall.
3. Some maintained at least moderate to expected levels of reproductive recruitment even during the worst dry years, while others showed little or no recruitment until the near-average rains returned.
Conceptually, how should a population behave during severe climate change-like conditions, and can we use those responses to identify and/or validate the location of climate refugia?
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I think in Costa Rica there are some examples with bats in Monteverde.
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Hi. I want to assess the diversity of fish from 10 sampling sites at temporal scales. However, at each site I am going to use only 1 fishing gear to catch the fishes. If I want to prepare 5 replicates per site/sampling, is it valid if I take the replicates from the same fishing gear? I'm planning to use stratified random sampling technique to prepare these replicates. Thank you. 
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depends on the species under selection
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Insect-pest component in most of the models is weak, in some model dealt through input of pest population at various stages of crop growth to assess the yield loss associated with crops and cropping systems.
there is a need to develop population dynamics for major insect-pests of a region, and integrate with the crop simulation tools.
,my interest is to know the related work on this important aspect?
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In such studies it is more important (than just having a good pest model) to regard the pest as an 'indicator' of the maldesign and mismanagement of the agroecosystem (and to do research to correct these causes) rather than just as an 'enemy' to be controlled within such systems - the attached materials support these ideas - Supportively, Stuart
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I am looking for a quick and easy proxy measurement to estimate age in Boophone disticha (L.f.) Herb. for demographic monitoring. The number of leaf scales in a bulb can be used for this purpose, but I would prefer a non-invasive method that disturbs the plants as little as possible.
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Thank you all for your comments and suggestions. Back to the drawing board!
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We are creating a customized PVA program that can predict the extinction threshold depending on the years input. Some example and specific scientific data needed are current population of the crocodiles, age structure, vital rates, mortality rates, hatchlings, carrying capacity, mean survival for each stage, sex ratio, number of occupied patches, and etc. 
The only PVA factor we can only do is Density dependence. Spatial PVA related is not included.
If you guys have existing PVA studies of other animals, it would help us construct a program for it. We can change the subject animal because of the limited data of crocs. Any help would be appreciated.   
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Hello,
SPOMSIM may be a model you also can use. MetaConnect also but it may be harder. You can have a look on the paper of Gunton et al. 2016 in JAE (Multi-criterion trade-offs and synergies for spatial conservation planning) for more details.
Best regards
Sylvain
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I am calculating biomass differentiation associated with different fishing mortalities but it is too hard to understand the FAO or other resources.
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We have distribution data for larvae of 2 species that were first ransdomly spread on an experimental square arena. After some time, the area was divided in 100 quadrats of 1*1cm and the number of larvae of each species in each quadrat was counted.
What is the best way (i.e. agregation index) to 1/evidence that the observed distribution of larvae is aggregative and 2/evidence that this agregation is interspecific? There are several index and methods reported in the litterature, but I was unable to find the best way to answers these 2 questions according to our dataset (quadrat).
Thanks
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Deep-water fishes are commonly considered widespread and not a speciose group, but could this conservative group be used as a good (or even the best) indicative of a center of speciation? The recent increase of deep-water elasmobranch descriptions and records off the Brazilian coast (119 to 162 species from 1989 to 2007) has raised the question if there is a much higher diversity than previously expected. 
What group of fishes will be considered for your project? Thank you very much!
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I am uncertain whether deep water fishes can be considered more diversified or not, but part of the problem rely on the level of knowledge of certain regions in specific oceans. For instance, in some places (e.g., the Pacific coast of South America) deep sea fishes are very incompletely known. Some recent deep-sea expeditions have discovered many new fishes (most of which still remain undescribed) but also new records for non-deep see waters. In this case, many of those new records have been explained as results of El Nino or La Nina Phenomenon or currents that have significant changes in the marine biodiversity. To my best knowledge, the area of origin of a group is something more complex than to be explained using number of species in an specific area (e.g., deep-sea waters).
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Logistic population models can be used for human population dynamics. The model have a factor. usually denoted by K, the carrying capacity. 
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There's a power law relatioship between body size and maximum density population (K) in mammals group: Damuth's Law. This paper tried to verify this to human populations but there isn't a single equation to measure K for humans based on empirical data (unfortunately).
To extrapolate K, you could plot the logistics equations changing the parameters and find the best fit (minimal residuals), and take the values for r and K.  After this, take the second derivative (dN2/dt2) from diferential equation with the parameters found before, and compare with the derivative fom experimental data. The behavior of this second derivative is a parabola, so the derivative from empirical data should be a "quase-parabola". The empirical and theoretical should be superimposed. 
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Not so complicated. Hizimura & Matsuyama (1999) suggested a model to characterize and describe eruptions (crashes) in population dynamics. I'm testing some hypothesis and I'd like to test the second derivative (equation 7) and compare with some time series. Unfortunately they didn't calculate this. Anyone here likes a mathematical trouble? If you want to help me I could describe such details. Thanks a lot.
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Uau! A little monster. It'll be very useful to ecology community. Now let's go to experiments. Thanks Sir Esipov for you attention and to be very helpful.  If I can reward you in some way...
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I have microsat data for multiple fish populations, and I'd like to get the best estimates of effective population size as I possibly can, but my problem is I have no age or size class data. Therefore my dataset contains overlapping generations, and I have no way of discerning what individuals are of what age class. Is there an accepted way to calculate Ne despite this overlap? Most every method I see requires you have age class information or discrete generations. Any help is appreciated.
Thanks!
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Mistake just above, link should be http://www.panearth.org/
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Simulation model for population of Koalas?
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Try articles at http://www/panearth.org;
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Scholars of Demography
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Professor Dr.N.Audinarayana is a famous Demographer, he wrote many books about Population projection, dynamics etc.  Try to contact directly his mail ID: audinarayana.bu@gmail.com
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Hey there,
I want to analyse the diversity of two populations with SSR markers.
I managed to calculate the FST value with the hierfstat package. But now I want to test whether FST is significant. I tried to do this by bootstrapping with the function boot.ppfst, but I don't understand the output (lots of pairwise tables). And I don't know how I can get something like a p-value that tells me whether my FST value is significance or not.
Does anyone know how this works? Is there a R script available?
Thank you!
Judith
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Hi, Judith!
Moreover, you still can use boot.ppfst to test the significance of your FST value.
Previously, you have gotten many tables because you have many markers and these tables are Variance Components.
Try to do it:
1) Create a object to store your output [eg: my_object <- boot.ppfst(dataset, nboot=your_choice, quant=c("your_choice_lowvalue", "your_choice_highvalue")]
2) Take a look in Confidence Interval Upper Limit and in Lower Limit using:
- my_object$ul
- my_object$ll
Each command you will see a SIMPLE TABLE 2x2 with only ONE NUMERIC VALUE because you have only TWO POPULATIONS. So, in all your analysis you expect only 3 VALUES (FST Value between two pops, UL Conf. Interv. and LL Conf. Interval)
What do you mean with "significance of FST"?
This sounds like a hypothesis. So you can say "This FST value is significantly different of ZERO (0.00)?"
Using Confidence Interval you can check it. If your Confidence Interval (Lower Limit - Upper Limit) include ZERO, so your FST value IS NOT significantly different of ZERO (null).
Take a look in two numeric examples:
1) FST = 0.07 (UL = 0.10; LL = 0.04)
2) FST = 0.09 (UL=0.15; LL = - 0.03 minus)
In the first case the FST Value IS SIGNIFICANTLY different of ZERO.
In the second case the FST Value IS NOT SIGNIFICANTLY different of ZERO.
Sorry about the long text and sorry if I wasn't enough clear or if I was not polite in any moment. It is late night here (laughs!).
Best regards,
João Paulo Gomes Viana
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There are severals predictions about that populations with a history of inbreeding would show less inbreeding depression (because deleterious mutations had been purged) tha populations with little or no prior inbreeding.The history of inbreeding was inferred by severals methods, including average inbreeding coefficients, morphology flower and populations size, and others. Inbreeding depression was usually estimated by comparing fitness components in experimental inbred populations to noninbred populations and estimating of severals parameters in the populations.
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I'm looking for data or information on the yearly fluctuations of duck populations (and other anatidae) in Alaska. Do numbers stay roughly the same or can there be major differences from year to year? Thanks
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Hi Edouard, you should take a look at the Waterfowl Breeding Population and Habitat Survey data that is hosted on the Migratory Bird data center of the USFWS.
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I'm (as HEAD of BIOMONITORING laboratory) looking for interested scientists to assist us in the preparation of articles and in future on collaboration work. We need qualified help in English and possible statistical analysis. Interests ornithology (population dynamics, spatial heterogeneity, climate change), ichthyology (fish populations(assemblages) and environmental parameters). Only without money relations help is welcome. The opportunity to be a co-author of articles only is welcomed. Post-docs, young PhD, PhD student from Europe and North America is welcomed.
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For ornithology I can help I am working on other topics as well, but I have done ornithology in the past and supervising a student at this time as well.
if you still need help contact me at floris.breman@wur.nl
good luck
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I have used STRUCTURE and BIMR to describe population structure and migration rates. But, now I want to identify individual migrants within these sampled populations.
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yes there is a "posteriori" assignment test in Geneclass2. I've run it LOOOONG time ago so I'm not sure how it is in the current version, but it wasn't hard to interpret at all. The main question is to have "GOOD reference populations" to assign these potential immigrants to. So what I was suggested to do when I've applied it, is to use as reference pops the genetic clusters resulting from STRUCTURE. Thus, the likely immigrants would be like "ouliers" if you can say that way from those in theory "panmitic" genetic units resulting from Structure. But is fast to run, so you can run both with the genetic clusters and the populations they were captured in....  and interpret the results  from both assignments.
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I want to get a age structur of a population without having informations about growth rates.
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I use the software FISAT with "Laa" programme. it give a good results on crustacean species
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Hi,
I want to analyse the lymphocyte population by flow cytometry, however my staining and cell population dynamics are being affected due to too many tumor cells present in the lung. Is there any way to remove these? 
P.S. I can't use lymphocyte enrichment kit as it may remove the cell population of interest as well.
Thanks!
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Hi,
I have worked with tumors and I find that ficoll density gradient centrifugation removes most of the bulk debris and gives u an enriched lymphocyte fraction. However u end up with few cells for analysis. But this is sufficient for flowcytometry. Percoll should function in a similar way. This is the best that u can do. Otherwise u need to purify CD3 cells using MACS.
Best,
Anchana