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Which components grow in size but not in number and which grow in number but not in size?

Which components grow in size but not in number and which grow in number but not in size?



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During growth of an individual animal some components of the body grow in size but not in number (type 1) while some others increase in number but not in size (type 2). Which of the following is correct? (A) type 1: bones and muscle cells; type 2: hair follicles, red blood cells and epithelial cells. (B) type 1: bones and red blood cells; type 2: hair follicles, muscle cells and epithelial cells. (C) type 1: hair follicles and muscle cells; type 2: bones, red blood cells and epithelial cells. (D) type 1: epithelial cells and bones; type 2: hair follicles, red blood cells and muscle cells.

I know muscle cells only grow in size. I am unsure about hair follicles, which is why I am confused between options A and C. I am also confused about bone growth. Does it ever increase in number? It would be nice if someone could clear that up.


We navigated through this question with Fenil in comments, I'll resume the answer here:

Osteocytes

We know from this page that "osteocytes have an average half life of 25 years, they do not divide" and that "when osteoblasts become trapped in the matrix that they secrete, they become osteocytes. Osteocytes are networked to each other via long cytoplasmic extensions that occupy tiny canals called canaliculi".

We can thus conclude that bone cells don't grow in numbers, but in size by their cytoplasmic extensions.

Hair follicles

Well, we draw the conclusion that hair follicles grow in number and not in size based on the following common sense observations:

  • We don't get to see tiny hair follicles in babies and giant ones in adults;
  • If they don't grow in size, either they grow in number or they become less and less dense as the skin surface grows with age. If the latter, we should see the same hair number in babies and in adults, whichever their height is, and this is obviously not the case.

So, they grow in number.

The answer is $A$.


Characteristics of Living Organism: Growths, Reproduction and Metabolism | Biology

Living organisms have ability to grow, reproduce, to sense environment and provide a suitable response. Livings organism have attributes like metabolism, ability to self-replicate, self-organize, interact and emergence. All living organisms grow, increase in mass and increase in number of individuals having twin characteristics of growth.

A multicellular organism grows by division of cells. Growth by cell division occurs throughout their life span. In plants, this growth is seen only up to a certain age. In animals, however, cell division occurs in certain tissues to replace lost cells. Unicellular organisms also grow by cell division.

In most of the higher animals and plants, growth and reproduction are mutually exclusive events. A dead organism does not grow. Development is a sequential phenomenon. It is directed by genetic code. One stages after another which is irreversible process.

Growth is defined as increase in size and mass during the development of an organism over a period of time. It is measured as an increase in biomass and is associated with cell division by mitosis, subsequent increases in cell size, and with the differentiation of cells to perform particular functions.

Reproduction:

Reproduction is the process of production of progeny possessing features more or less similar to their parents in multicellular organisms. Reproduction in organisms occurs by asexual means also. Fungi multiply and spread easily due to the millions of asexual spores they produce. When it comes to unicellular organisms like bacteria, unicellular algae, reproduction is synonymous with growth.

That is increase in number of cells. There are many organisms which are not able to reproduce for example infertile human couples, mules, sterile worker bees, etc. However, only living organisms have capability to reproduce, not non-living objects.

Reproduction is the natural process among organisms by which new individuals are generated and the species perpetuated.

Metabolism:

All living organisms are comprised of chemicals. These chemicals are constantly being made and changed into some other bimolecular. Thousands of metabolism reactions used to occur simultaneously inside all living organisms. It may be unicellular or multicellular.

All plants, animals, fungi and microbes have metabolism. All the chemical reactions occurring in our body is metabolism. Environment senses through sense organs. Plants respond to light, water, temperature, other organisms, pollutants, etc.

All organisms handle chemicals entering their bodies. Human being is the only organism who has self-consciousness. Properties of tissues are not present in the constituent cells, but they arise as a result of interactions among the constituent cells. Living organisms are self-replicating, evolving and self- regulating interactive systems capable of responding to external stimuli.

Metabolism have two stages-catabolism and anabolism. Catabolism is the process of breaking large molecules into small molecules. Anabolism is the process where chemical reactions occur and which lead to production of large molecules by splitting small molecules.

Metabolism is the sum of physical and chemical processes in an organism by which protoplasm is produced, maintained and destroyed, and by which energy is made available for its functioning.


You have 2 ways of doing this:

If you set flex-grow to 0 you are telling the items to keep their width and expand the empty space. Using flex-grow: 1 the items will expand the width to fit the space.

invisible items (fiddle)

Add some invisible items after the normal items with the same flex properties. You will get a left aligned look.

In this solution there will be the same number of items in all visible rows so make sure to add enough invisible items to work properly on larger screens.

I can't reply to Igor's answer to suggest an improvement to his solution, but his fiddle did not work entirely for a responsive layout.

The first solution with flex-grow: 0 doesn't stretch the items to fill the containers. The second solution with invisible items works, but expands the height of the container. Removing the top/bottom margins did the trick though.

I edited the jsfiddle, to make it both responsive and fix the container-expanding invisible items, using comments to explain what I changed. It still doesn't work if the container is larger than the row of items though: (fiddle)

I also made my own simple solution. I was trying to do this myself, and Igor's solution helped immensely. (fiddle)


Which components grow in size but not in number and which grow in number but not in size? - Biology

The amount of satellite cells present within in a muscle depends on the type of muscle. Type I or slow-twitch oxidative fibers, tend to have a five to six times greater satellite cell content than Type II (fast-twitch fibers), due to an increased blood and capillary supply (2). This may be due to the fact that Type 1 muscle fibers are used with greatest frequency, and thus, more satellite cells may be required for ongoing minor injuries to muscle.

Immunology
As described earlier, resistance exercise causes trauma to skeletal muscle. The immune system responds with a complex sequence of immune reactions leading to inflammation (3). The purpose of the inflammation response is to contain the damage, repair the damage, and clean up the injured area of waste products.
The immune system causes a sequence of events in response to the injury of the skeletal muscle. Macrophages, which are involved in phagocytosis (a process by which certain cells engulf and destroy microorganisms and cellular debris) of the damaged cells, move to the injury site and secrete cytokines, growth factors and other substances. Cytokines are proteins which serve as the directors of the immune system. They are responsible for cell-to-cell communication. Cytokines stimulate the arrival of lymphocytes, neutrophils, monocytes, and other healer cells to the injury site to repair the injured tissue (4).

The three important cytokines relevant to exercise are Interleukin-1 (IL-1), Interleukin-6 (IL-6), and tumor necrosis factor (TNF). These cytokines produce most of the inflammatory response, which is the reason they are called the “inflammatory or proinflammatory cytokines” (5). They are responsible for protein breakdown, removal of damaged muscle cells, and an increased production of prostaglandins (hormone-like substances that help to control the inflammation).

Growth Factors
Growth factors are highly specific proteins, which include hormones and cytokines, that are very involved in muscle hypertrophy (6). Growth factors stimulate the division and differentiation (acquisition of one or more characteristics different from the original cell) of a particular type of cell. In regard with skeletal muscle hypertrophy, growth factors of particular interest include insulin-like growth factor (IGF), fibroblast growth factor (FGF), and hepatocyte growth factor (HGF). These growth factors work in conjunction with each other to cause skeletal muscle hypertrophy.

Insulin-Like Growth Factor
IGF is a hormone that is secreted by skeletal muscle. It regulates insulin metabolism and stimulates protein synthesis. There are two forms, IGF-I, which causes proliferation and differentiation of satellite cells, and IGF-II, which is responsible for proliferation of satellite cells. In response to progressive overload resistance exercise, IGF-I levels are substantially elevated, resulting in skeletal muscle hypertrophy (7).

Fibroblast Growth Factor
FGF is stored in skeletal muscle. FGF has nine forms, five of which cause proliferation and differentiation of satellite cells, leading to skeletal muscle hypertrophy. The amount of FGF released by the skeletal muscle is proportional to the degree of muscle trauma or injury (8).

Hepatocyte Growth Factor
HGF is a cytokine with various different cellular functions. Specific to skeletal muscle hypertrophy, HGF activates satellite cells and may be responsible for causing satellite cells to migrate to the injured area (2).
Hormones in Skeletal Muscle Hypertrophy
Hormones are chemicals which organs secrete to initiate or regulate the activity of an organ or group of cells in another part of the body. It should be noted that hormone function is decidedly affected by nutritional status, foodstuff intake and lifestyle factors such as stress, sleep, and general health. The following hormones are of special interest in skeletal muscle hypertrophy.

Growth Hormone
Growth hormone (GH) is a peptide hormone that stimulates IGF in skeletal muscle, promoting satellite cell activation, proliferation and differentiation (9). However, the observed hypertrophic effects from the additional administration of GH, investigated in GH-treated groups doing resistance exercise, may be less credited with contractile protein increase and more attributable to fluid retention and accumulation of connective tissue (9).

Cortisol
Cortisol is a steroid hormone (hormones which have a steroid nucleus that can pass through a cell membrane without a receptor) which is produced in the adrenal cortex of the kidney. It is a stress hormone, which stimulates gluconeogenesis, which is the formation of glucose from sources other than glucose, such as amino acids and free fatty acids. Cortisol also inhibits the use of glucose by most body cells. This can initiate protein catabolism (break down), thus freeing amino acids to be used to make different proteins, which may be necessary and critical in times of stress.
In terms of hypertrophy, an increase in cortisol is related to an increased rate of protein catabolism. Therefore, cortisol breaks down muscle proteins, inhibiting skeletal muscle hypertrophy (10).

Testosterone
Testosterone is an androgen, or a male sex hormone. The primary physiological role of androgens are to promote the growth and development of male organs and characteristics. Testosterone affects the nervous system, skeletal muscle, bone marrow, skin, hair and the sex organs.
With skeletal muscle, testosterone, which is produced in significantly greater amounts in males, has an anabolic (muscle building) effect. This contributes to the gender differences observed in body weight and composition between men and women. Testosterone increases protein synthesis, which induces hypertrophy (11).

Fiber Types and Skeletal Muscle Hypertrophy
The force generated by a muscle is dependent on its size and the muscle fiber type composition. Skeletal muscle fibers are classified into two major categories slow-twitch (Type 1) and fast-twitch fibers (Type II). The difference between the two fibers can be distinguished by metabolism, contractile velocity, neuromuscular differences, glycogen stores, capillary density of the muscle, and the actual response to hypertrophy (12).

Type I Fibers
Type I fibers, also known as slow twitch oxidative muscle fibers, are primaritly responsible for maintenance of body posture and skeletal support. The soleus is an example of a predominantly slow-twitch muscle fiber. An increase in capillary density is related to Type I fibers because they are more involved in endurance activities. These fibers are able to generate tension for longer periods of time. Type I fibers require less excitation to cause a contraction, but also generate less force. They utilize fats and carbohydrates better because of the increased reliance on oxidative metabolism (the body’s complex energy system that transforms energy from the breakdown of fuels with the assistance of oxygen) (12).
Type I fibers have been shown to hypertrophy considerably due to progressive overload (13,15). It is interesting to note that there is an increase in Type I fiber area not only with resistance exercise, but also to some degree with aerobic exercise (14).

Type II Fibers
Type II fibers can be found in muscles which require greater amounts of force production for shorter periods of time, such as the gastrocnemius and vastus lateralis. Type II fibers can be further classified as Type IIa and Type IIb muscle fibers.

Type IIa Fibers
Type IIa fibers, also known as fast twitch oxidative glycolytic fibers (FOG), are hybrids between Type I and IIb fibers. Type IIa fibers carry characteristics of both Type I and IIb fibers. They rely on both anaerobic (reactions which produce energy that do not require oxygen), and oxidative metabolism to support contraction (12).
With resistance training as well as endurance training, Type IIb fibers convert into Type IIa fibers, causing an increase in the percentage of Type IIa fibers within a muscle (13). Type IIa fibers also have an increase in cross sectional area resulting in hypertrophy with resistance exercise (13). With disuse and atrophy, the Type IIa fibers convert back to Type IIb fibers.

Type IIb Fibers
Type IIb fibers are fast-twitch glycolytic fibers (FG). These fibers rely solely on anaerobic metabolism for energy for contraction, which is the reason they have high amounts of glycolytic enzymes. These fibers generate the greatest amount of force due to an increase in the size of the nerve body, axon and muscle fiber, a higher conduction velocity of alpha motor nerves, and a higher amount of excitement necessary to start an action potential (12). Although this fiber type is able to generate the greatest amount of force, it is also maintains tension for a shortesst period of time (of all the muscle fiber types).
Type IIb fibers convert into Type IIa fibers with resistance exercise. It is believed that resistance training causes an increase in the oxidative capacity of the strength-trained muscle. Because Type IIa fibers have a greater oxidative capacity than Type IIb fibers, the change is a positive adaptation to the demands of exercise (13).

Conclusion
Muscular hypertrophy is a multidimensional process, with numerous factors involved. It involves a complex interaction of satellite cells, the immune system, growth factors, and hormones with the individual muscle fibers of each muscle. Although our goals as fitness professionals and personal trainers motivates us to learn new and more effective ways of training the human body, the basic understanding of how a muscle fiber adapts to an acute and chronic training stimulus is an important educational foundation of our profession.


Table 1. Structural Changes that Occur as a Result of Muscle Fiber Hypertrophy
Increase in actin filaments
Increase in myosin filaments
Increase in myofibrils
Increase in sarcoplasm
Increase in muscle fiber connective tissue
Source: Wilmore, J.H. and D. L. Costill. Physiology of Sport and Exercise (2nd Edition).Champaign, IL: Human Kinetics, 1999.

References

1. Russell, B., D. Motlagh,, and W. W. Ashley. Form follows functions: how muscle shape is regulated by work. Journal of Applied Physiology 88: 1127-1132, 2000.

2. Hawke, T.J., and D. J. Garry. Myogenic satellite cells: physiology to molecular biology. Journal of Applied Physiology. 91: 534-551, 2001.

3. Shephard, R. J. and P.N. Shek. Immune responses to inflammation and trauma: a physical training model. Canadian Journal of Physiology and Pharmacology 76: 469-472, 1998.

4. Pedersen, B. K. Exercise Immunology. New York: Chapman and Hall Austin: R. G. Landes, 1997.

5. Pedersen, B. K. and L Hoffman-Goetz. Exercise and the immune system: Regulation, Integration, and Adaptation. Physiology Review 80: 1055-1081, 2000.

6. Adams, G.R., and F. Haddad. The relationships among IGF-1, DNA content, and protein accumulation during skeletal muscle hypertrophy. Journal of Applied Physiology 81(6): 2509-2516, 1996.

7. Fiatarone Singh, M. A., W. Ding, T. J. Manfredi, et al. Insulin-like growth factor I in skeletal muscle after weight-lifting exercise in frail elders. American Journal of Physiology 277 (Endocrinology Metabolism 40): E135-E143, 1999.

8. Yamada, S., N. Buffinger, J. Dimario, et al. Fibroblast Growth Factor is stored in fiber extracellular matrix and plays a role in regulating muscle hypertrophy. Medicine and Science in Sports and Exercise 21(5): S173-180, 1989.

9. Frisch, H. Growth hormone and body composition in athletes. Journal of Endocrinology Investigation 22: 106-109, 1999.

10. Izquierdo, M., K Hakkinen, A. Anton, et al. Maximal strength and power, endurance performance, and serum hormones in middle-aged and elderly men. Medicine and Science in Sports Exercise 33 (9): 1577-1587, 2001.

11. Vermeulen, A., S. Goemaere, and J. M. Kaufman. Testosterone, body composition and aging. Journal of Endocrinology Investigation 22: 110-116, 1999.

12. Robergs, R. A. and S. O. Roberts. Exercise Physiology: Exercise, Performance, and Clinical Applications. Boston: WCB McGraw-Hill, 1997.

13. Kraemer, W. J., S. J. Fleck, and W. J. Evans. Strength and power training: physiological mechanisms of adaptation. Exercise and Sports Science Reviews 24: 363-397, 1996.

14. Carter, S. L., C. D. Rennie, S. J. Hamilton, et al. Changes in skeletal muscle in males and females following endurance training. Canadian Journal of Physiology and Pharmacology 79: 386-392, 2001.

15. Hakkinen, K., W. J. Kraemer, R. U. Newton, et al. Changes in electromyographic activity, muscle fibre and force production characteristics during heavy resistance/power strength training in middle-aged and older men and women. Acta Physiological Scandanavia 171: 51-62, 2001.


Mitosis

Interphase

(technically not part of mitosis, but it is included in the cell cycle)

Cell is in a resting phase, performing cell functions

Organelles double in number, to prepare for division

Prophase

1. chromosomes visible (chromatids)
2. centrioles migrate to the poles
3. nuclear membrane disappears
4. nucleolus disappears
5. spindle form

Metaphase

Chromosomes line up along the equator

Anaphase

Chromatids separate and move to opposite poles

1. chromosomes disappear (becoming chromatin)

2. nuclear membrane reforms

Cytokinesis

- division of the cytoplasm to form 2 new daughter cells

- daughter cells are genetically identical

- cells return to interphase

. cytokinesis takes two forms, depending on the cell.

Animal Cells - cell pinches inward and then splits into two

Plants - a new cell wall (called the cell plate) forms between the two new cells


The Future of World Religions: Population Growth Projections, 2010-2050

The religious profile of the world is rapidly changing, driven primarily by differences in fertility rates and the size of youth populations among the world’s major religions, as well as by people switching faiths. Over the next four decades, Christians will remain the largest religious group, but Islam will grow faster than any other major religion. If current trends continue, by 2050 …

  • The number of Muslims will nearly equal the number of Christians around the world.
  • Atheists, agnostics and other people who do not affiliate with any religion – though increasing in countries such as the United States and France – will make up a declining share of the world’s total population.
  • The global Buddhist population will be about the same size it was in 2010, while the Hindu and Jewish populations will be larger than they are today.
  • In Europe, Muslims will make up 10% of the overall population.
  • India will retain a Hindu majority but also will have the largest Muslim population of any country in the world, surpassing Indonesia.
  • In the United States, Christians will decline from more than three-quarters of the population in 2010 to two-thirds in 2050, and Judaism will no longer be the largest non-Christian religion. Muslims will be more numerous in the U.S. than people who identify as Jewish on the basis of religion.
  • Four out of every 10 Christians in the world will live in sub-Saharan Africa.

These are among the global religious trends highlighted in new demographic projections by the Pew Research Center. The projections take into account the current size and geographic distribution of the world’s major religions, age differences, fertility and mortality rates, international migration and patterns in conversion.


As of 2010, Christianity was by far the world’s largest religion, with an estimated 2.2 billion adherents, nearly a third (31%) of all 6.9 billion people on Earth. Islam was second, with 1.6 billion adherents, or 23% of the global population.

If current demographic trends continue, however, Islam will nearly catch up by the middle of the 21st century. Between 2010 and 2050, the world’s total population is expected to rise to 9.3 billion, a 35% increase. 1 Over that same period, Muslims – a comparatively youthful population with high fertility rates – are projected to increase by 73%. The number of Christians also is projected to rise, but more slowly, at about the same rate (35%) as the global population overall.

As a result, according to the Pew Research projections, by 2050 there will be near parity between Muslims (2.8 billion, or 30% of the population) and Christians (2.9 billion, or 31%), possibly for the first time in history. 2

With the exception of Buddhists, all of the world’s major religious groups are poised for at least some growth in absolute numbers in the coming decades. The global Buddhist population is expected to be fairly stable because of low fertility rates and aging populations in countries such as China, Thailand and Japan.

Worldwide, the Hindu population is projected to rise by 34%, from a little over 1 billion to nearly 1.4 billion, roughly keeping pace with overall population growth. Jews, the smallest religious group for which separate projections were made, are expected to grow 16%, from a little less than 14 million in 2010 to 16.1 million worldwide in 2050.

Adherents of various folk religions – including African traditional religions, Chinese folk religions, Native American religions and Australian aboriginal religions – are projected to increase by 11%, from 405 million to nearly 450 million.

And all other religions combined – an umbrella category that includes Baha’is, Jains, Sikhs, Taoists and many smaller faiths – are projected to increase 6%, from a total of approximately 58 million to more than 61 million over the same period. 3

While growing in absolute size, however, folk religions, Judaism and “other religions” (the umbrella category considered as a whole) will not keep pace with global population growth. Each of these groups is projected to make up a smaller percentage of the world’s population in 2050 than it did in 2010. 4

Similarly, the religiously unaffiliated population is projected to shrink as a percentage of the global population, even though it will increase in absolute number. In 2010, censuses and surveys indicate, there were about 1.1 billion atheists, agnostics and people who do not identify with any particular religion. 5 By 2050, the unaffiliated population is expected to exceed 1.2 billion. But, as a share of all the people in the world, those with no religious affiliation are projected to decline from 16% in 2010 to 13% by the middle of this century.

At the same time, however, the unaffiliated are expected to continue to increase as a share of the population in much of Europe and North America. In the United States, for example, the unaffiliated are projected to grow from an estimated 16% of the total population (including children) in 2010 to 26% in 2050.

As the example of the unaffiliated shows, there will be vivid geographic differences in patterns of religious growth in the coming decades. One of the main determinants of that future growth is where each group is geographically concentrated today. Religions with many adherents in developing countries – where birth rates are high, and infant mortality rates generally have been falling – are likely to grow quickly. Much of the worldwide growth of Islam and Christianity, for example, is expected to take place in sub-Saharan Africa. Today’s religiously unaffiliated population, by contrast, is heavily concentrated in places with low fertility and aging populations, such as Europe, North America, China and Japan.

Globally, Muslims have the highest fertility rate, an average of 3.1 children per woman – well above replacement level (2.1), the minimum typically needed to maintain a stable population. 6 Christians are second, at 2.7 children per woman. Hindu fertility (2.4) is similar to the global average (2.5). Worldwide, Jewish fertility (2.3 children per woman) also is above replacement level. All the other groups have fertility levels too low to sustain their populations: folk religions (1.8 children per woman), other religions (1.7), the unaffiliated (1.7) and Buddhists (1.6).

Another important determinant of growth is the current age distribution of each religious group – whether its adherents are predominantly young, with their prime childbearing years still ahead, or older and largely past their childbearing years.

In 2010, more than a quarter of the world’s total population (27%) was under the age of 15. But an even higher percentage of Muslims (34%) and Hindus (30%) were younger than 15, while the share of Christians under 15 matched the global average (27%). These bulging youth populations are among the reasons that Muslims are projected to grow faster than the world’s overall population and that Hindus and Christians are projected to roughly keep pace with worldwide population growth.

All the remaining groups have smaller-than-average youth populations, and many of them have disproportionately large numbers of adherents over the age of 59. For example, 11% of the world’s population was at least 60 years old in 2010. But fully 20% of Jews around the world are 60 or older, as are 15% of Buddhists, 14% of Christians, 14% of adherents of other religions (taken as a whole), 13% of the unaffiliated and 11% of adherents of folk religions. By contrast, just 7% of Muslims and 8% of Hindus are in this oldest age category.

In addition to fertility rates and age distributions, religious switching is likely to play a role in the growth of religious groups. But conversion patterns are complex and varied. In some countries, it is fairly common for adults to leave their childhood religion and switch to another faith. In others, changes in religious identity are rare, legally cumbersome or even illegal.

The Pew Research Center projections attempt to incorporate patterns in religious switching in 70 countries where surveys provide information on the number of people who say they no longer belong to the religious group in which they were raised. In the projection model, all directions of switching are possible, and they may be partially offsetting. In the United States, for example, surveys find that some people who were raised with no religious affiliation have switched to become Christians, while some who grew up as Christians have switched to become unaffiliated. These types of patterns are projected to continue as future generations come of age. (For more details on how and where switching was modeled, see the Methodology. For alternative growth scenarios involving either switching in additional countries or no switching at all, see Chapter 1.)

Over the coming decades, Christians are expected to experience the largest net losses from switching. Globally, about 40 million people are projected to switch into Christianity, while 106 million are projected to leave, with most joining the ranks of the religiously unaffiliated. (See chart above.)

All told, the unaffiliated are expected to add 97 million people and lose 36 million via switching, for a net gain of 61 million by 2050. Modest net gains through switching also are expected for Muslims (3 million), adherents of folk religions (3 million) and members of other religions (2 million). Jews are expected to experience a net loss of about 300,000 people due to switching, while Buddhists are expected to lose nearly 3 million.

International migration is another factor that will influence the projected size of religious groups in various regions and countries.

Forecasting future migration patterns is difficult, because migration is often linked to government policies and international events that can change quickly. For this reason, many population projections do not include migration in their models. But working with researchers at the International Institute for Applied Systems Analysis in Laxenburg, Austria, the Pew Research Center has developed an innovative way of using data on past migration patterns to estimate the religious composition of migrant flows in the decades ahead. (For details on how the projections were made, see Chapter 1.)

The impact of migration can be seen in the examples shown in the graph at the right, which compares projection scenarios with and without migration in the regions where it will have the greatest impact. In Europe, for instance, the Muslim share of the population is expected to increase from 5.9% in 2010 to 10.2% in 2050 when migration is taken into account along with other demographic factors that are driving population change, such as fertility rates and age. Without migration, the Muslim share of Europe’s population in 2050 is projected to be nearly two percentage points lower (8.4%). In North America, the Hindu share of the population is expected to nearly double in the decades ahead, from 0.7% in 2010 to 1.3% in 2050, when migration is included in the projection models. Without migration, the Hindu share of the region’s population would remain about the same (0.8%).

In the Middle East and North Africa, the continued migration of Christians into the six Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates) is expected to offset the exodus of Christians from other countries in the region. 7 If migration were not factored into the 2050 projections, the estimated Christian share of the region’s population would drop below 3%. With migration factored in, however, the estimated Christian share is expected to be just above 3% (down from nearly 4% in 2010).

Beyond the Year 2050

This report describes how the global religious landscape would change if current demographic trends continue. With each passing year, however, there is a chance that unforeseen events – war, famine, disease, technological innovation, political upheaval, etc. – will alter the size of one religious group or another. Owing to the difficulty of peering more than a few decades into the future, the projections stop at 2050.

Readers may wonder, though, what would happen to the population trajectories highlighted in this report if they were projected into the second half of this century. Given the rapid projected increase from 2010 to 2050 in the Muslim share of the world’s population, would Muslims eventually outnumber Christians? And, if so, when?

The answer depends on continuation of the trends described in Chapter 1. If the main projection model is extended beyond 2050, the Muslim share of the world’s population would equal the Christian share, at roughly 32% each, around 2070. After that, the number of Muslims would exceed the number of Christians, but both religious groups would grow, roughly in tandem, as shown in the graph above. By the year 2100, about 1% more of the world’s population would be Muslim (35%) than Christian (34%).

The projected growth of Muslims and Christians would be driven largely by the continued expansion of Africa’s population. Due to the heavy concentration of Christians and Muslims in this high-fertility region, both groups would increase as a percentage of the global population. Combined, the world’s two largest religious groups would make up more than two-thirds of the global population in 2100 (69%), up from 61% in 2050 and 55% in 2010.

It bears repeating, however, that many factors could alter these trajectories. For example, if a large share of China’s population were to switch to Christianity (as discussed in this sidebar), that shift alone could bolster Christianity’s current position as the world’s most populous religion. Or if disaffiliation were to become common in countries with large Muslim populations – as it is now in some countries with large Christian populations – that trend could slow or reverse the increase in Muslim numbers.

Regional and Country-Level Projections

In addition to making projections at the global level, this report projects religious change in 198 countries and territories with at least 100,000 people as of 2010, covering 99.9% of the world’s population. Population estimates for an additional 36 countries and territories are included in regional and global totals throughout the report. The report also divides the world into six major regions and looks at how each region’s religious composition is likely to change from 2010 to 2050, assuming that current patterns in migration and other demographic trends continue. 8

Due largely to high fertility, sub-Saharan Africa is projected to experience the fastest overall growth, rising from 12% of the world’s population in 2010 to about 20% in 2050. The Middle East-North Africa region also is expected to grow faster than the world as a whole, edging up from 5% of the global population in 2010 to 6% in 2050. Ongoing growth in both regions will fuel global increases in the Muslim population. In addition, sub-Saharan Africa’s Christian population is expected to double, from 517 million in 2010 to 1.1 billion in 2050. The share of the world’s Christians living in sub-Saharan Africa will rise from 24% in 2010 to 38% in 2050.

Meanwhile, the Asia-Pacific region is expected to have a declining share of the world’s population (53% in 2050, compared with 59% in 2010). This will be reflected in the slower growth of religions heavily concentrated in the region, including Buddhism and Chinese folk religions, as well as slower growth of Asia’s large unaffiliated population. One exception is Hindus, who are overwhelmingly concentrated in India, where the population is younger and fertility rates are higher than in China or Japan. As previously mentioned, Hindus are projected to roughly keep pace with global population growth. India’s large Muslim population also is poised for rapid growth. Although India will continue to have a Hindu majority, by 2050 it is projected to have the world’s largest Muslim population, surpassing Indonesia.

The remaining geographic regions also will contain declining shares of the world’s population: Europe is projected to go from 11% to 8%, Latin American and the Caribbean from 9% to 8%, and North America from 5% to a little less than 5%.

Europe is the only region where the total population is projected to decline. Europe’s Christian population is expected to shrink by about 100 million people in the coming decades, dropping from 553 million to 454 million. While Christians will remain the largest religious group in Europe, they are projected to drop from three-quarters of the population to less than two-thirds. By 2050, nearly a quarter of Europeans (23%) are expected to have no religious affiliation, and Muslims will make up about 10% of the region’s population, up from 5.9% in 2010. Over the same period, the number of Hindus in Europe is expected to roughly double, from a little under 1.4 million (0.2% of Europe’s population) to nearly 2.7 million (o.4%), mainly as a result of immigration. Buddhists appear headed for similarly rapid growth in Europe – a projected rise from 1.4 million to 2.5 million.

In North America, Muslims and followers of “other religions” are the fastest-growing religious groups. In the United States, for example, the share of the population that belongs to other religions is projected to more than double – albeit from a very small base – rising from 0.6% to 1.5%. 9 Christians are projected to decline from 78% of the U.S. population in 2010 to 66% in 2050, while the unaffiliated are expected to rise from 16% to 26%. And by the middle of the 21st century, the United States is likely to have more Muslims (2.1% of the population) than people who identify with the Jewish faith (1.4%). 10

In Latin America and the Caribbean, Christians will remain the largest religious group, making up 89% of the population in 2050, down slightly from 90% in 2010. Latin America’s religiously unaffiliated population is projected to grow both in absolute number and percentage terms, rising from about 45 million people (8%) in 2010 to 65 million (9%) in 2050. 11

Changing Religious Majorities

Several countries are projected to have a different religious majority in 2050 than they did in 2010. The number of countries with Christian majorities is expected to decline from 159 to 151, as Christians are projected to drop below 50% of the population in Australia, Benin, Bosnia-Herzegovina, France, the Netherlands, New Zealand, the Republic of Macedonia and the United Kingdom.

Muslims in 2050 are expected to make up more than 50% of the population in 51 countries, two more than in 2010, as both the Republic of Macedonia and Nigeria are projected to gain Muslim majorities. But Nigeria also will continue to have a very large Christian population. Indeed, Nigeria is projected to have the third-largest Christian population in the world by 2050, after the United States and Brazil.

As of 2050, the largest religious group in France, New Zealand and the Netherlands is expected to be the unaffiliated.

About These Projections

While many people have offered predictions about the future of religion, these are the first formal demographic projections using data on age, fertility, mortality, migration and religious switching for multiple religious groups around the world. Demographers at the Pew Research Center in Washington, D.C., and the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria, gathered the input data from more than 2,500 censuses, surveys and population registers, an effort that has taken six years and will continue.

The projections cover eight major groups: Buddhists, Christians, Hindus, Jews, Muslims, adherents of folk religions, adherents of other religions and the unaffiliated (see Appendix C: Defining the Religious Groups). Because censuses and surveys in many countries do not provide information on religious subgroups – such as Sunni and Shia Muslims or Catholic, Protestant and Orthodox Christians – the projections are for each religious group as a whole. Data on subgroups of the unaffiliated are also unavailable in many countries. As a result, separate projections are not possible for atheists or agnostics.

The projection model was developed in collaboration with researchers in the Age and Cohort Change Project at IIASA, who are world leaders in population projections methodology. The model uses an advanced version of the cohort-component method typically employed by demographers to forecast population growth. It starts with a population of baseline age groups, or cohorts, divided by sex and religion. Each cohort is projected into the future by adding likely gains (immigrants and people switching in) and by subtracting likely losses (deaths, emigrants and people switching out) year by year. The youngest cohorts, ages 0-4, are created by applying age-specific fertility rates to each female cohort in the childbearing years (ages 15-49), with children inheriting the mother’s religion. For more details, see the Methodology. 12

In the process of gathering input data and developing the projection model, the Pew Research Center previously published reports on the current size and geographic distribution of major religious groups, including Muslims (2009), Christians (2011) and several other faiths (2012). An initial set of projections for one religious group, Muslims, was published in 2011, although it did not attempt to take religious switching into account.

Some social theorists have suggested that as countries develop economically, more of their inhabitants will move away from religious affiliation. While that has been the general experience in some parts of the world, notably Europe, it is not yet clear whether it is a universal pattern. 13 In any case, the projections in this report are not based on theories about economic development leading to secularization.

Rather, the projections extend the recently observed patterns of religious switching in all countries for which sufficient data are available (70 countries in all). In addition, the projections reflect the United Nations’ expectation that in countries with high fertility rates, those rates gradually will decline in coming decades, alongside rising female educational attainment. And the projections assume that people gradually are living longer in most countries. These and other key input data and assumptions are explained in detail in Chapter 1 and the Methodology (Appendix A).

Since religious change has never previously been projected on this scale, some cautionary words are in order. Population projections are estimates built on current population data and assumptions about demographic trends, such as declining birth rates and rising life expectancies in particular countries. The projections are what will occur if the current data are accurate and current trends continue. But many events – scientific discoveries, armed conflicts, social movements, political upheavals, natural disasters and changing economic conditions, to name just a few – can shift demographic trends in unforeseen ways. That is why the projections are limited to a 40-year time frame, and subsequent chapters of this report try to give a sense of how much difference it could make if key assumptions were different.

For example, China’s 1.3 billion people (as of 2010) loom very large in global trends. At present, about 5% of China’s population is estimated to be Christian, and more than 50% is religiously unaffiliated. Because reliable figures on religious switching in China are not available, the projections do not contain any forecast for conversions in the world’s most populous country. But if Christianity expands in China in the decades to come – as some experts predict – then by 2050, the global numbers of Christians may be higher than projected, and the decline in the percentage of the world’s population that is religiously unaffiliated may be even sharper. (For more details on the possible impact of religious switching in China, see Chapter 1.)

Finally, readers should bear in mind that within every major religious group, there is a spectrum of belief and practice. The projections are based on the number of people who self-identify with each religious group, regardless of their level of observance. What it means to be Christian, Muslim, Hindu, Buddhist, Jewish or a member of any other faith may vary from person to person, country to country, and decade to decade.

Acknowledgements

These population projections were produced by the Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation.

Many staff members in the Pew Research Center’s Religion & Public Life project contributed to this effort. Conrad Hackett was the lead researcher and primary author of this report. Alan Cooperman served as lead editor. Anne Shi and Juan Carlos Esparza Ochoa made major contributions to data collection, storage and analysis. Bill Webster created the graphics and Stacy Rosenberg and Ben Wormald oversaw development of the interactive data presentations and the Global Religious Futures website. Sandra Stencel, Greg Smith, Michael Lipka and Aleksandra Sandstrom provided editorial assistance. The report was number-checked by Shi, Esparza Ochoa, Claire Gecewicz and Angelina Theodorou.

Several researchers in the Age and Cohort Change project of the International Institute for Applied Systems Analysis collaborated on the projections, providing invaluable expertise on advanced (“multistate”) population modeling and standardization of input data. Marcin Stonawski wrote the cutting-edge software used for these projections and led the collection and analysis of European data. Michaela Potančoková standardized the fertility data. Vegard Skirbekk coordinated IIASA’s research contributions. Additionally, Guy Abel at the Vienna Institute of Demography helped construct the country-level migration flow data used in the projections.

Over the past six years, a number of former Pew Research Center staff members also played critical roles in producing the population projections. Phillip Connor prepared the migration input data, wrote descriptions of migration results and methods, and helped write the chapters on each religious group and geographic region. Noble Kuriakose was involved in nearly all stages of the project and helped draft the chapter on demographic factors and the Methodology. Former intern Joseph Naylor helped design maps, and David McClendon, another former intern, helped research global patterns of religious switching. The original concept for this study was developed by Luis Lugo, former director of the Pew Research Center’s Religion & Public Life project, with assistance from former senior researcher Brian J. Grim and visiting senior research fellow Mehtab Karim.

Others at the Pew Research Center who provided editorial or research guidance include Michael Dimock, Claudia Deane, Scott Keeter, Jeffrey S. Passel and D’Vera Cohn. Communications support was provided by Katherine Ritchey and Russ Oates.

We also received very helpful advice and feedback on portions of this report from Nicholas Eberstadt, Henry Wendt Scholar in Political Economy, American Enterprise Institute Roger Finke, Director of the Association of Religion Data Archives and Distinguished Professor of Sociology and Religious Studies, The Pennsylvania State University Carl Haub, Senior Demographer, Population Reference Bureau Todd Johnson, Associate Professor of Global Christianity and Director of the Center for the Study of Global Christianity, Gordon Conwell Theological Seminary Ariela Keysar, Associate Research Professor and Associate Director of the Institute for the Study of Secularism in Society and Culture, Trinity College Chaeyoon Lim, Associate Professor of Sociology, University of Wisconsin-Madison Arland Thornton, Research Professor in the Population Studies Center, University of Michigan Jenny Trinitapoli, Assistant Professor of Sociology, Demography and Religious Studies, The Pennsylvania State University David Voas, Professor of Population Studies and Acting Director of the Institute for Social and Economic Research, University of Essex Robert Wuthnow, Andlinger Professor of Sociology and Director of the Center for the Study of Religion, Princeton University and Fenggang Yang, Professor of Sociology and Director of the Center on Religion and Chinese Society, Purdue University.

While the data collection and projection methodology were guided by our consultants and advisers, the Pew Research Center is solely responsible for the interpretation and reporting of the data.

Roadmap to the Report

The remainder of this report details the projections from multiple angles. The first chapter looks at the demographic factors that shape the projections, including sections on fertility rates, life expectancy, age structure, religious switching and migration. The next chapter details projections by religious group, with separate sections on Christians, Muslims, the religiously unaffiliated, Hindus, Buddhists, adherents of folk or traditional religions, members of “other religions” (consolidated into a single group) and Jews. A final chapter takes a region-by-region look at the projections, including separate sections on Asia and the Pacific, Europe, Latin America and the Caribbean, the Middle East and North Africa, North America and sub-Saharan Africa.


References

  1. C. S. Ng et al., “Growth of respiratory droplets in cold and humid air,” Phys. Rev. Fluids 6, 054303 (2021).
  2. K. L. Chong et al., “Extended lifetime of respiratory droplets in a turbulent vapor puff and its implications on airborne disease transmission,” Phys. Rev. Lett. 126, 034502 (2021).

Growth of respiratory droplets in cold and humid air

Chong Shen Ng, Kai Leong Chong, Rui Yang, Mogeng Li, Roberto Verzicco, and Detlef Lohse


What are growth rates?

The label &ldquogrowth rate&rdquo is broad in that it refers to the change of a specific variable over a predefined time period. Owners typically express growth as a percentage. Growth rates can provide you with a more accurate depiction of financial health, especially when comparing percentage growth to industry rates.

Narrowing growth rate down to a percentage will level the playing field so that you can measure yourself against others in the industry. For instance, imagine you’re a small business selling a new tech product. If you were to compare your revenue to established tech firms, you’d probably find yourself lagging considerably.

But, if you notice that your yearly growth rate percentage is significantly higher than the growth rates of other firms, you can conclude that you’re more efficient with resources and have a more successful product.

The standard growth rate formula is straightforward. If you’re looking to use it to measure future value, the equation expressed in percentage form is:

Projected growth rate = ((Targeted future value – Present value) / (Present value)) * 100

So, let’s say that you are currently producing $50,000 in sales but want to reach $125,000. Your growth rate formula would be:

Growth Rate (Future) = ($125,000 – $50,000) / ($50,000) * 100 = 150%

This formula merely shows that you need to grow by 150% to meet your goal. You can also add time periods to the equation. All you need to do is divide your calculated growth rate by the number of periods you’d like to measure. This is called the annual rate.

For example, imagine that you want to grow to $125,000 in sales within three years. You’d like to figure out the monthly growth factor. Divide 150% by 36 to yield a monthly growth rate of 4.17%. You’ll need to exhibit a positive percentage change of 4.17 per month if you wish to hit your sales goal on time.

You can also calculate the growth rate as a measure of past performance. In these situations, the equation is:

Growth rate (past) = ((Present value – Past value) / (Past value)) * 100

If you add the number of periods into the equation, this allows you to determine the percentage increase or decrease that you displayed over any number of years.

Now that we’ve covered growth rates at the most basic levels, let’s look at in-depth ways to comprehend them. There are two different ways to understand growth rate &mdash average annual growth rate and compound annual growth rate.

Average annual growth rate

The average annual growth rate (AAGR) is the average increase of a variable during the course of a calendar year. It’s an excellent tool to help measure average growth over a year. Variables that you can use when measuring AAGR include:

The formula to calculate AAGR is:

AAGR = ((Growth rate period A) + (Growth rate period B) + (Growth rate period N)) / (Number of payments)

The average growth rate is particularly useful when predicting ending values and long-term trends. It allows small business owners and potential investors to evaluate the average percentage change that occurred over time. This allows them to predict ending values based on past performance.

Compound annual growth rate

The compound annual growth rate (CAGR) provides the rate of return necessary to grow investments, assuming that all profits and dividends are reinvested. Compound growth measures how long it takes to grow from its beginning value to its final value. The CAGR formula is:

Compound annual growth rate = ((Ending balance/Beginning balance) ^ (1 / Number of years)) – 1

If you have something that can rise or fall in value over time, then you’ll want to measure percentage changes using CAGR. The tool can help compare rates of returns from one investment versus another &mdash say, a high-yield savings account versus a stock.

CAGR measures things in a perfect world, meaning the investment grows at the same rate every year, and you reinvest the profits each year. Although this may not always be the case with an asset like stocks, you can still use CAGR to understand and predict returns more quickly.


THE EFFECT OF TEMPERATURE ON GROWTH RATE AND ADULT SIZE ACCORDING TO OPTIMAL RESOURCE ALLOCATION MODELING

The effect of temperature in an aseasonal environment under instantaneous switch from growth to reproduction

It was shown earlier that it is optimal in an aseasonal environment to switch completely and irreversibly from growth to reproduction when condition (9) is satisfied. The condition contains the derivative of the quotient for the production/mortality rates with respect to body size. If mortality is size-independent, condition (9) simplifies to condition (10), saying that at optimal adult size the derivative of the production rate with respect to body size expressed in energy units must be equal to the mortality rate. If an optimal resource allocation model can explain the temperature-size rule, temperature must work through modification of the mortality and/or production components of the optimal condition (10).

There exist data indicating that the body size exponents for the resource acquisition and metabolic rates change with temperature (see Discussion). Let us now assume that constants a1 and a2 of production equation (8) increase with temperature, but exponents b1 and b2 decrease. An example is shown in Figure 4. The parameters change according to the rules shown in the insert of Figure 4. As a result, the curves describing the production rate as a function of body mass (expressed in energy units) cross at the size of roughly 250 units: below this size the production rate increases with temperature, but above it decreases with temperature ( Fig. 4). Reaction norms of optimal adult sizes, dependent on both the production rate and mortality rate, are shown in the figure. For different mortality rates we get different shapes of the reaction norms of adult sizes: for high mortality m = 0.0009, adult size slightly increases with temperature for m = 0.0008, adult size is almost invariant (not shown), then for lower mortality rates, between 0.0007 and 0.0005, adult size decreases more and more with temperature. If the mortality rate is further decreased, the sizes for low temperature are shifted to the right of the production rate intercrosses. Although the rule of the lower size in higher temperature is still fulfilled, in fact the production rate (and growth rate as a result) is higher for low than for high temperature, which is against the rule that larger sizes are attained under lower growth rates in cold. Thus there exists a broad but limited range of mortality rates for which the temperature-size rule is fulfilled. Such a range of mortality rates can always be found if production rate increases with size more rapidly at low than at high temperatures. Obviously such a pattern of production curves calls for biological explanations (see Discussion). However, production efficiency decreases with temperature in the model in ectotherms this is the exception rather than the rule ( Angilletta and Dunham, 2003).

Let us assume now that the constant a1 of the resource acquisition rate increases and the constant a2 of the metabolic rate decreases with temperature, but exponents b1 and b2 behave the opposite way, according to the rules shown in the insert in Figure 5. (Although a2 decreases with temperature, the metabolic rate increases with temperature because of accompanying changes in b2.) The curves describing the production rate as a function of body mass intercross, but unlike the previous example along a broader range of sizes ( Fig. 5). For high mortality m = 0.0009, adult size increases with temperature for m = 0.0007 and m = 0.0006, adult size increases and then decreases with temperature and for m = 0.0005, adult size monotonically decreases with temperature. Thus the temperature-size rule is fulfilled not only for a limited range of mortality rates, but also, at some mortalities, for a limited range of temperatures. Interestingly, production efficiency increases with temperature in this example.

Bertalanffy (1960) assumed that the parameters α and β in (1), but not the exponents, change with temperature. For bioenergetic model (8) it is equivalent to changing constants a1 and a2 with temperature (insert in Fig. 6). Under such an assumption the production curves do not cross if Q10 of the assimilation and metabolic rates are the same ( Fig. 6). The temperature-size rule can be caused in such a case by the temperature-dependence of the mortality rate, as suggested by Atkinson (1994) and Atkinson and Sibly (1997). The insert in Figure 6 shows three curves depicting the increase of mortality with temperature. The reaction norm of adult size to temperature depends on the rate at which mortality rises with temperature. If this increase is slow, that is, with Q10 lower than for the bioenergetic parameters, optimal sizes increase with temperature if the increase is moderate, that is, with Q10 the same as for the bioenergetic parameters, optimal sizes are temperature-invariant but when mortality increases with temperature rapidly, that is, with Q10 higher than for the bioenergetic parameters, optimal sizes become larger in cold ( Fig. 6). Q10 for the assimilation and metabolic rate constants applied in this numerical example, roughly 1.3, are realistic but low ( Angilletta et al., 2004). For numerous species with higher assimilation and respiration Q10, mortality rate Q10 also must be higher to make the decrease of size with temperature optimal.

There is no reason to assume that the temperature-size rule is caused either by the exponents of the production equation changing with temperature (as in Figs. 4 and 5) or by mortality changing with temperature (as in Fig. 6). It is possible for both mechanisms to work simultaneously, which makes the rule even more likely to appear, because the temperature sensitivity of mortality and bioenergetic parameters needed to produce a size decrease with temperature may be lower than when only one mechanism is engaged.

The effect of temperature in an aseasonal environment under a graded switch from growth to reproduction

Under the range of parameters studied, growth trajectories always have three phases: pure growth, mixed growth and reproduction (with slower growth as a result), and pure reproduction. The insert in Figure 7B shows the growth trajectories and duration of these phases in different temperatures. The growth rate in the first phase always increases with temperature, but prolonged growth in cold makes larger size optimal, expressed both as size at maturity and as final size ( Fig. 7B). Thus the temperature-size rule can also be satisfied in an aseasonal environment under a graded switch.

The effect of temperature in a seasonal environment

To test how the proposed temperature-dependence of the production parameters shapes life histories in a seasonal environment, we use Kozłowski's (1996a) resource allocation model. Briefly, the model organisms repeatedly experience summers of length 200 days (T) in which growth and reproduction is possible, followed by 165-day winters with zero production rate. Yearly survival is defined by the summer and winter mortality rates (ms, mw) according to the equation p = exp(−msT)exp[−mw(365 − T)]. The production rate at body size w expressed in energy units is defined by equation (8). The model finds the maximum body size wmax, beyond which it is never optimal to grow, and the lifetime pattern of resource allocation to growth and reproduction in previous seasons.

Figure 8 outlines the shape of the optimal growth curves (thick lines) generated from the model for three temperatures, and the potential growth trajectories if all production was devoted to growth only (thin lines). Temperature was assumed to affect parameters a1, b1, a2, b2 of production in the same way as in the example in Figure 4 for aseasonal conditions. The effects of temperature on the optimal growth pattern were analyzed across the mortality gradient ( Fig. 8A, B, C). In general, a lower mortality rate extends the juvenile period, leading to larger size at maturity wmat and final size wmax, and it enlarges the fraction of final size attained at maturity (larger wmat/wmax). Under low mortality ( Fig. 8A), a rising temperature, despite accelerating the growth rate in the juvenile period, selects for earlier maturation at smaller size and smaller final size. The fraction of final size attained at maturity rises with temperature the ratio wmat/wmax is equal to 0.57, 0.59 and 0.66 at 5, 15 and 25°C. The figure clearly shows that rising temperature decreases the sizes wmat and wmax, leading to the “temperature-size rule,” not because growth potential decreases with size but because increasingly fewer resources are devoted to growth to maximize the expected lifetime allocation to reproduction. Under high mortality ( Fig. 8C), the temperature effect on life histories is just the reverse of the one observed under low mortality. Interestingly, under medium mortality ( Fig. 8B), the temperature-size rule is not fulfilled for size at maturity, but does hold for final size.


Key Companies & Market Share Insights

Marijuana products provide various medical benefits to the users, which has increased their adoption for the treatment of various chronic conditions. Hence, they are gaining traction within a short time. Moreover, competition in the global market is high because companies are focused on increasing their product offerings, entering new markets, and gaining new consumers.

The market is expanding at a rapid pace as a large number of European and Asian countries have legalized the medical use of marijuana. The first movers in the market are expected to capitalize by increasing their regional presence and consolidating their market share. Various regulatory, quality and pricing norms are also impacting cross-border trade. Some prominent players in the global legal marijuana market include: