Population Dynamics - Science topic

Conserving biodiversity, especially in the current scenario, is a significant challenge for conservationists, policy planners, and global diplomats. However, it is possible to mitigate the biodiversity manipulation through careful planning, monitoring, and adaptability. Firstly, raising awareness among people who are unaware of the importance of these species in their life are crucial and requires extensive awareness campaigns at regional scales according to the manipulation occur in particular area. Secondly, a balanced and inclusive approach involving local communities are essential. sustainable land management practices should be adopted, particularly in the Indian Himalayan Region (IHR). Possible solutions include the resettlement and rehabilitation of tribal communities, providing alternative livelihood opportunities, and cultivating non-timber forest products and medicinal plants through scientific approaches and ensure the people they get appropriate technical as well as financial adds by concern authority for doing so government role will be mandatory . Additionally, establishing buffer zones, implementing both ex situ and in situ conservation methods, conducting research, and maintaining regular monitoring and enforcement measures are vital for biodiversity conservation. investment of money, political will and provide alternative livelihood is crucial step to conserve the plant diversity of species before it's too late.

Good day Frank, this question is about optimality based 1776 traditional market and going from optimality assumptions and no population problems to later over production, over consumption, and over population dynamics problems creating environmental problems/system stability problems, where markets and population dynamics are assumed to be independent factors, capture in the question:

Frank, good day. Thank you for commenting.

Can you please read the question carefully, if is about whether the independency assumption explains the origins of the over population problem.

As you said, there was no over population problem and then there was if markets and populations dynamics are independent of each other, how do you go from no population problem to over population problem, that why the question is:

Houssine Soussi, thanks for your feedback on the issue of a decrease in population through a visionary immigration policy. It is both extensive and insightful. By balancing economic expansion with cultural responsiveness, you stress not only attracting skilled workers but also establishing an assorted and encompassing society. The concept of “talent hubs” that have been put forward, which have simplified procedures as well as support systems, can facilitate immigration for everyone. Unique elements from different countries across the globe make it a more empathetic and comprehensive approach to addressing demographic issues facing South Korea.

Dear Xiaojie and Michael. Thank for commenting. I asked the question with the variables market dynamics and population dynamics and their impact on development problems in mind. If taken as independent variables it leads to having two independent independent root causes driving system stability, positive or negative. If the nature of populations dynamics is driven by the nature of market dynamics, then there is only one root cause affecting system stability as then populations dynamics, positive or negative is a consequence of the nature of market dynamics, positive or negative.

As you may know the traditional view is that they are independent variables, a view that began in 1776 with Adam Smith traditional market thinking, which continues today with frameworks like the UN responsible production and responsible consumption framework. On the other hand, extreme population dynamics based frameworks like the ecological overshoot work under market dynamics neutrality assumptions. I think that facts show that those variables are not independent, which leads to a systematic view of how the issue of system stability like environmental problems should have been looked out by the Brundtland commission in 1987 "our common future", but instead it was looked out through sustainable development theory that works under population dynamics neutrality assumptions. So if dependent variables are taken as independent variables it would lead to distorted policy recommendation and compartamentalization.

Thank you for commenting.

Lucio

To use age data in FiSAT II software, you will need to follow these steps:

It is important to note that age data should be used cautiously in FiSAT II, as it can introduce bias into the analysis if not properly accounted for. It is recommended to use age data in combination with length data, as this can provide a more accurate representation of the fish community.

I would recommend not thinking of t₀ as the age at zero length (theoretical or otherwise) but simply as a convenient adjustment that allows the von Bertalanffy function to better fit age/length data from rather older individuals. Fish are not obliged to grow according to the von B function. It is only a useful approximation to their biology and it is one that does not fit well to the growth patterns of the very young -- hence the need for a t₀ parameter. If, however, t₀ is only a convenient adjustment, then it has no meaning outside of a particular fit of the von B function to a particular dataset and there is no point in trying to calculate a value for it, other than as a side product of fitting the equation.

Dariusz, the way you try to share your material is not helpful for sharing ideas ...you say Yes, In will leave it there.

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.

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

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.

Please take a look at this useful RG link.

Thank you, Caleb A. Aldridge !

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.

I hope the following is interesting to you

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:

https://www.bbc.com/news/science-environment-12811197#:~:text=A%20study%20using%20census%20data,set%20for%20extinction%2C%20say%20researchers.&text=The%20result%2C%20reported%20at%20the,out%20altogether%20in%20those%20countries.

Thank you for this wonderful opportunity to share my views.

Best wishes and regards

Yoganandan

Advances and Applications in Nonlinear Control Systems

Sample Size Rule

Sekaran (2013) wrote:

"Roscoe (1975) proposes the following rules of thumb for determining sample size:

Sekaran, U., 2003. Research methods for business: A skill building approach. John Wiley & Sons.

Dear Quinn Carvey

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

Dear Scott G. Ward

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.

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

Hi Ozodbek. That is exactly what my hypothesis aims to solve: it explains how it is actually an endogenous population-wide multi-year hormone cycle that causes the population cycles.

It is obvious that the environment affects the population size, but the proposed evolutionary mechanism is apart from those changes that are caused by the environment, because the multi-year hormonal fluctuations do manifest even without predators and if there is ample food (this has been proven by experiments by Charles Krebs and others).

The mystery of regular multi-year population cycles has been studied for over a century, but I believe I have possibly finally solved it, and what is much more interesting to me is that it looks like many human populations are undergoing this very same multi-year population-wide hormone cycle. I could be wrong, but the evidence is mounting up to support the hypothesis.

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.

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

I second @Thierry. The site should help you with all you need

It is what i want!

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

Many thanks for all your answers ( Enrique Reynaldo de la Cruz , Kevin M. Fraley)

Dear Shian-Loong,

Thank you for quick reply. I understand what you mean now by spatial "clustering". Are you aware of any publications that look at the individual as decision making entity but consider the fact that these live in actual households. In my case, I am considering actual census data, which is on the individual/citizen level. It is probably better to estimate propensities/probabilities (to use them later in simulation) on the individual level. However, if I directly use them in a simulation, I am ignoring the fact that they might make a decision on the household level.

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:

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.

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

Hi Gerlien, I had 5 populations (sampled) and a population was assumed as "GHOST".

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.

You can go through the cited references for ideas.

Depends....common polling techniques occur over time and place, and they are useful for increasing the number of observation when the N is low.

The free software Vortex (http://www.vortex10.org/Vortex10.aspx) is quite handy for your analyse if you have the main bioparameters.

Please also have a look at this useful ResearchGate link.

this link is useful

https://www.environment.gov.au/.../tetratheca-juncea-guidelines.pdf

regards

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.

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.

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.

Dear Dr. Oum, thank you so much for these documents. I will immediately study them.

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

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

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

hi see this attach about this model

> notonecta.dbmem1 <- dbmem(pond_coord, thresh=0.01707804, MEM.autocor = "non-null", store.listw = TRUE, silent = FALSE)

[1] 0.14567211 0.04095702 -0.03654004 -0.09026366 -0.09084915 -0.09090909 -0.09090909

[8] -0.09090915 -0.14911688 -0.18256433

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

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,

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 :)

This is better explained here

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.

We're using the methods that our department is used to..active searching. Capture-mark-recapture.

InsightMaker would work. It is very easy to use.

Please check thee useful PDF attachments.

I think in Costa Rica there are some examples with bats in Monteverde.

depends on the species under selection

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

Thank you all for your comments and suggestions. Back to the drawing board!

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

see

Dear Damien,

i suggest to you links and attached files in topics.

- Aggregation Behaviour and Interspecific Responses in Three Species ...

www.scielo.br/scielo.php?script=sci_arttext&pid=S0074...

- Intra- and interspecific aggregation among dung beetles - Cambridge ...

Best regards

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).

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. 

http://www.econ.ku.dk/dalgaard/Work/Bioeconomic.pdf 

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...

Mistake just above, link should be http://www.panearth.org/

Try articles at http://www/panearth.org;

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

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:

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:

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

The short answer is yes. Compare Crnokrak and Barrett (2003) for a review of the evidence for purging that includes plants: https://www.researchgate.net/publication/10902470_Crnokrak_P_Barrett_SCH_Purging_the_genetic_load_a_review_of_the_experimental_evidence_Evolution_56_2347-2358

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.

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