Population Projection - Science topic

thank you for your feedback

We used CT in agricultural fields and setasides/game fields to count small game species and meso predators.

In front of the CT we did cut (in accordance with the farmers) a "window", a small clearance of 5x5m.

I recommend to read the several publications/guidances of ENETWILD.

These population statistics can be obtained from Indian Statistical Institute (ISI) Kolkotta

Ministry of Education or Ministry of Human Resources, New Delhi might have the population data as you need.

The suggestion indicates the hypothesis testing(mostly same), And it needs prior information, that whether the mean is equal to some value. Does the significance test of the coefficient work in this case?

Sounds like a question to be answered by a review of relevant literature. Best, D. Booth

Altough not exactly related to your research question, you may find Acemoglu & Restrepo (2017) useful as a first reference. You can check the paper here https://www.nber.org/papers/w23077

Imed Miraoui yes I believe in the foreseeable future steel industry would still be polluting as much even though they have taken many steps in advancing technologies

Sjoerd van Acht and Yehia F. Khalil thank you for the recommendation. would you happen to have the article that you could pass on to me? It would be much appreciated

Please take a look at the following references.

If you can uniquely identify your animals in the images, then there is a large body of literature on spatially explicit capture recapture which provides a solid framework for estimating animal density (in a spatially explicit way, potentially as a function of additional covariates) - The following reference provided a good soft introduction to the topic

Borchers, D. L. 2012 A non-technical overview of spatially explicit capture-recapture models Journal of Ornithology, 152 , 435-444

and this second one (is one of many examples that) provides details about camera trap studies:

Tobler, M. W. & Powell, G. V. 2013

Estimating jaguar densities with camera traps: Problems with current designs and recommendations for future studies Biological Conservation, , 159 , 109-118

This will work for crocodiles or any other species that can be uniquely identifianble from the photos. A diferent approach would be using distance sampling related approaches, and those are described here for camera traps:

Howe, E. J.; Buckland, S. T.; Després-Einspenner, M.-L. & Kühl, H. S. 2017.

Distance sampling with camera traps Methods in Ecology and Evolution, 2017

Finally, I don't know the deatils about this one, but the title suggest it might be what you need if individuals can't be identified uniquely - this will possible require some strong assumptions

Nakashima, Y.; Fukasawa, K. & Samejima, H. 2017

Estimating animal density without individual recognition using information derivable exclusively from camera traps Journal of Applied Ecology,

Hope it helps!

Dear Khan,

The earth has and will always have enough "for everyone’s need but not for everyone’s greed". Therefore, the question is not about overpopulation but it has more to do with over-consumption and overproduction. It seems that for those with privilege it is easy to talk about overpopulation (human beings as if they were undesirable) and a few billions too many. Yet, those who have less in the world are the ones who exploit resources the least and conserve biodiversity the most. Unfortunately, the world's hunger is the results of wars and conflicts, not because of population growth. There is hunger and yet the amount of wasted food per year can feed the world four times over. Those who insist on talking about overpopulation are only finding a way to blame others and to divert attention away from greed and planetary vandalism that destroy our natural resources and take away what little is left from the 'have-nots'.

This is what they say: "There is overpopulation and therefore there are not enough resources for everybody." The eyes of some immediately turn to the growing populations in emerging and developing nations. Yes, there is population growth but it is not unsustainable if the wars and global mega-corruption will stop. In Japan and the West there is low birth rate; Russia is such a big country with less than 160 million people, and so on. Therefore, when people talk about overpopulation, they are referring to the others (those they deem undesirable).

It is easy to fall into the trap of going along with this narrative when they use mind games through imagery to suggest that the poor are giving birth to too many children even though there is not enough food to feed them and there are many diseases, too.

Consider this. Where there is industrialization and opportunities for all, birth rate tends to be low. Even in developing nations, the middle and upper middle classes have fewer children. It must be noted, however, that with or without food the privileged don’t like the poor anyway.

Solutions:

- Arrest greed, over-consumption and overproduction and don’t steal from the have-nots.

- Good nutrition can already solve most of global health problems – Waste not, want not!!.

- Let’s create opportunities for all so that girls can aspire to be anything they want to be - after college and Master’s or a possibility to learn a trade, no one has time for ten children per family. When girls have opportunities to go to college just like their male counterparts, they tend to also want to become professors, doctors, engineers, pilots, entrepreneurs. etc., because they can.

- Men should take responsibility for family planning: when women are left bereft of educational/vocational opportunities, men use them as breeding machines. Women don’t make the children all by themselves - and no child should suffer any indignation of being seen as the one who arrived in the world as one child too many.

Just my two cents.

Samberg, L. (2018). ‘World Hunger Is Increasing, Thanks to Wars and Climate Change’ Scientific American, October 18, 2017, available at: https://www.scientificamerican.com/article/world-hunger-is-increasing-thanks-to-wars-and-climate-change/ (accessed 12 September 2018)

World Hunger Is Increasing, Thanks to Wars and Climate ...

Scientific American is the essential guide to the most awe-inspiring advances in science and technology, explaining how they change our understanding of the world and shape our lives.

Deen, T. (2018). 'Food Waste Enough to Feed World’s Hungry Four Times Over', Inter Press Service, available at: http://www.ipsnews.net/2018/05/food-waste-enough-feed-worlds-hungry-four-times/ (accessed 3 June 2018).

Fred. Pearce…https://www.theguardian.com/commentisfree/2014/sep/19/not-overpopulation-that-causes-climate-change-but-overconsumption

Than you brother Robert Tolan.

Ok

well, it was worth a shot. noise is the threat that will bring this highway to the trials. The wetland will be 99% intact. The EA process is fine, but the fact that these questions always are left with a “best guess” bothers me, especially while there are so many models out there in the research community. Noise I perhaps a debated topic, but if you ask me, there is no question about whether Or not it has a sigonificant effect on avian communities.

i guess I’ll tackle it from a habitat availability approach then. the % habitat (maybe quality weighted) contribution of the wetland in question To the total availability in an area.

Antimicrobial resistance is multi-factorial. Sub-optimal exposure to antimicrobial, gene transfer (both horizontal and vertical), spontaneous mutation are among the many. When there is frequent infection, there will be exposure to broad spectrum antimicrobials which will result in selection pressure in which resistant strains are selected. These strains will constantly evolve and result in gene transfer, both horizontal and vertical. The spontaneous mutation also contribute to the emergence of resistance.

As suggested, juveniles often aren't included because they don't directly contribute to population growth until they reach adulthood and breed. An individual that doesn't breed doesn't affect the population's future.

It's also important to note that different methods are usually required to sample different life stages. Juveniles are often too small to be caught and marked, are less mobile and thus less likely to be caught, or aren't detectable by methods used to find adults such as call/song surveys for birds.

The importance of juvenile vs adult survival is a whole sub-field - check out this paper and the sources it cites, particularly Sandercock et al and Wilson and Martin, which address birds.

Hello Jacob

I am not a time series expert, but I have used them in my forecasting work. You ask two questions:

I would not expect this to be the case, as there is no reason why population size should influence population growth.

As a direct effect, a change of policy in period t-1 can be expected to also affect period t. However, a change of policy is not specifically contained in the population data for period t-1. Knowledge of the policy is additional information, which would have to be explicitly incorporated in the model. A substantial change in population size in period t-1 could be temporary (a migration event or change of rules that affects the pace of migrant acceptances - i.e. a tempo effect); this would be noise or could also be modelled by a time-specific dummy variable. So, I would answer 'no': as in economics, time series models involving only one step are atheoretical in population forecasting. However, it may be the case that large-scale in-migration of young adults in period t-1 generates additional population growth in a subsequent range of years through childbearing and the in-migration of fiances, spouses, children and other dependents, and this may possibly be modelled by time series, though forecasting would involve high uncertainty. It may be possible to establish more meaningful interpretations of population size with lags of a generation or so, for obvious reasons, but the age range of childbearing (even in low fertility populations) would weaken the association.

Hi Xavier,

I’m not an expert in newts, but I can imaging this paper could be interesting for you, and maybe you can find some answer for your questions. Look for the supplementary materials as well. Regarding your question about the behaviour response to capture, is easy to take it in account and use the appropiate model.

Best,

Jose

Sutherland, C. et al., 2016. Spatial Capture-Recapture: A Promising Method for Analyzing Data Collected Using Artificial Cover Objects. Herpetologica, 72(1), pp.000–000. Available at: http://www.hljournals.org/doi/abs/10.1655/HERPETOLOGICA-D-15-00027.

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

InsightMaker would work. It is very easy to use.

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

Yunfei, it depends on what is your further analysis. As you said, you obtained the same optimum k with or without these two genotypes. Hence, you may discard their informativeness (certainly they don't contribute much to the total informativeness, with more than 80% missing data). Otherwise, if the individuals are highly valuable for further analysis, you may keep them in the data set.

You can use a multilevel model to analyze the point data at level 1 and the county data at level 2 simultaneously - it is possible to use survey weights in such models see

and on multilevel more generally http://www.bristol.ac.uk/cmm/software/mlwin/mlwin-resources.html

It is  important to use such an approach - one reason is that there will be many more points than counties and the standard errors of the effect of variables at each level wil lneed to be calculated appropriately.

Inferring from counties to census tracts: there is a large literature on 'small area estimation'  

for ONE  way - see David Martin's grid-based approach

Take a look at this paper:  http://www.ncbi.nlm.nih.gov/pubmed/1784963

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!

Morris and Doak 2002 "Quantitative Conservation Biology" is a good intro to pop. viability analysis, goes through non-demographic and deterministic models through demographic and environmental stochastic PVA's, and comes with Matlab scripts you can get started with.

I am skeptical of the particle movement approach myself, and have had the opportunity to review manuscripts where estimates of abundance using this approach were very different than those simultaneously obtained using capture-recapture or mark resight approaches, which are more reliable.  One paper calculated estimates that differed by an order of magnitude, and I'm just not so sure that enough comparisons between this technique and more reliable approaches have occurred.  I would advocate use of an occupancy-based approach, where abundance (or density, if a buffer width with a biological basis can be confidently identified) is derived from differential presence/ absence (occupancy) and detection frequency parameters. In this scenario, the number of animals in each photo are best identified across a series of photos during a single trigger event.  However, even if not all can be accounted for, this number can be reasonably estimated assuming sufficient samples (i.e., photo events).  Hope this helps.