What Does a 35.1 BMI Mean?

A body mass index of 35.1 falls into an unhealthy range. A majority percent of the American population have a BMI that is in the overweight or obese weight ranges, according to the Weight-control Information Network. Having a healthy BMI is important as it not only improves the quality of your life but also reduces your risk for certain health problems.

35.1 is not a healthy BMI. Photo Credit bathroom scale image by sumos from Fotolia.com

Body Mass Index

The BMI helps you determine if you are at a healthy weight for your height. Using a simple calculation, the BMI places you into a healthy weight, overweight, underweight or obese category. The BMI is not a diagnostic tool. It is a screening tool for your health because it suggests where your weight falls. It does not diagnose any health problems.

Calculation and Categories

The BMI calculation is based on your height in inches and weight in pounds. While you can use an online calculator, you can also compute your BMI by hand. Take your weight in pounds and multiply it by 703. Then divide that number by your height in inches, and divide it again by your height in inches. The resulting number places you into your correct weight category. A BMI of 18.5 or below is underweight, 18.5 to 24.9 is a healthy weight, 25 to 29 is overweight and a BMI above 30 is obese.

Health Risks

A BMI of 35.1 places you into the obese category, which places you at risk for many health problems. As an obese person, you are more likely to suffer from diabetes, heart attack, heart disease, sleep apnea, depression and stroke. Day-to-day life may also be harder because simple tasks may become harder to accomplish when carrying around extra weight.

• Considerations

If your BMI is in the unhealthy range, consider making lifestyle changes. Choosing to eat a healthy diet filled with lean meats, whole grains, fruits, vegetables, low fat dairy products and nuts will help you lose weight. Monitoring your caloric intake by eating smaller-portion sizes will also help you lower your BMI. Exercise five days a week for 30 minutes each day to stay healthy and reach a better BMI.

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What Are Some Carbohydrates a Diabetic Should Not Eat?

Overview

While it’s important to eat some carbohydrates to help give your body the energy it needs, if you’re a diabetic, it’s also important to avoid certain types of carbohydrates that can raise your blood sugar too much and too quickly—those that have a high glycemic index, cautions the Harvard School of Public Health. Knowing which foods contain carbohydrates with a high glycemic index will help you plan healthy meals to manage your diabetes well.

A bowl of macaroni and cheese. Photo Credit bhofack2/iStock/Getty Images

Refined Grains

Foods made from refined grains contain carbohydrates that have a higher glycemic index than the carbs in foods made from whole grains, according to the American Diabetes Association. Some of the refined grains that have the highest glycemic index—of 70 or more—include macaroni and cheese, white bread, white rice, cornflakes, instant oatmeal, popcorn, pretzels and saltine crackers. Eating too many refined grains can raise blood sugar to dangerous levels, cautions the American Diabetes Association, so it’s best to replace refined grains with whole grains in your diet whenever possible.

Starchy Vegetables

While all non-starchy vegetables contain carbohydrates that have a low glycemic index of 55 or less, some starchy vegetables, such as potatoes and pumpkin, have high glycemic indexes of 70 or greater, the American Diabetes Association reports. So if you’re diabetic, you can help prevent your blood sugar level from rising too high by avoiding potatoes and pumpkin. Or, you can eat other vegetables in the same meal with potatoes or pumpkin to balance out the effect on your blood sugar. The American Diabetes Association says combining foods that have low-glycemic carbohydrates with foods that contain high-glycemic carbohydrates can allow you to enjoy high-glycemic foods without causing your blood sugar to spike.

Melon and Pineapple

Most fruits contain low-glycemic carbohydrates, says the American Diabetes Association, but two types of fruit have high glycemic indexes of 70 or greater: pineapple and melon, including watermelon, cantaloupe and honeydew. The Harvard School of Public Health notes that all types of fruits that are ripe tend to have a higher glycemic index than fruits that haven’t yet ripened, so if you’d like to indulge in a bit of melon or pineapple, your blood sugar will be less affected if you eat the fruit before it ripens.

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How to Lose Belly Fat With Yogurt

Replacing less-healthy foods with nonfat, plain yogurt may improve your weight-loss results. Although some research points toward weight loss when consuming nonfat yogurt -- including belly fat loss -- not all studies have found weight-loss benefits from eating yogurt. Yogurt does provide significant amounts of protein, calcium, phosphorus, potassium, zinc, vitamin B-12, riboflavin and pantothenic acid.

Glass jar of yogurt surrounded by blueberries, strawberries and raspberries.Photo Credit genious2000de/iStock/Getty Images

Potential Weight-Loss Benefits

A study published in "Obesity Research" in April 2004 found that compared to people on reduced-calorie diets who consumed 400 to 500 milligrams of calcium per day from dairy products, getting 1,200 to 1,300 milligrams of calcium per day from dairy products increased weight loss by approximately 70 percent and fat loss by about 64 percent -- with about 66 percent of fat loss coming from the trunk region of the body. Not all studies, however, show such drastic weight-loss improvements from eating more yogurt or dairy products. A study published in "Obesity Research" in October 2005 found that after a year, weight loss was about the same between the groups that consumed about one serving of dairy per day and those that consumed three or four servings per day.

Amount of Yogurt

A study published in the "International Journal of Obesity" in 2005 found that eating enough yogurt each day to consume 1,100 milligrams of calcium led to increases in both weight loss and belly fat loss compared to a control diet in which people consumed no more than 500 milligrams of calcium per day. Both groups ate reduced-calorie diets. An 8-ounce serving, or 1 cup, of plain nonfat yogurt has about 488 milligrams of calcium, so this is about 18 ounces of nonfat yogurt per day.

Type of Yogurt

The key to losing weight is to minimize the number of calories you consume, so nonfat plain yogurt is a better choice than low-fat or whole-milk yogurt. Nonfat plain yogurt has about 137 calories per cup, compared to 149 calories in the same quantity of whole-milk yogurt. Flavored yogurts contain more sugar, and thus, they contain more calories. Greek yogurt has about the same number of calories as regular yogurt if you choose nonfat, but whole Greek yogurt has about three times the fat of regular yogurt, making it higher in calories, as well. Greek yogurt also has only about one-third the calcium in regular yogurt, which means that you'd have to eat more for the potential weight-loss benefits.

Caloric Considerations

You'll need to replace something else in your diet with yogurt if you want to lose weight, rather than add yogurt to what you'd typically eat. If you eat 18 ounces of plain, nonfat yogurt, you'll get about 308 calories. Eliminate enough processed foods, sweets and sugar-sweetened beverages from your typical diet each day to make up for the increase in calories from yogurt.

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MYRMECOCHORY

Myrmecochory (/mɜːrmᵻˈkɒkɔːri/ (sometimes myrmechory); from Ancient Greek: μύρμηξ mýrmēksand χορεία khoreíā "circular dance") is seed dispersal by ants, an ecologically significant ant-plant interaction, with worldwide distribution. Most myrmecochorous plants produce seeds with elaiosomes a term encompassing various external appendages or "food bodies" rich in lipids amino acid, or other nutrients that are attractive to ants. The seed with its attached elaiosome is collectively known as a diaspore. Seed dispersal by ants is typically accomplished when foraging workers carry diaspores back to the ant colony after which the elaiosomes is removed or fed directly to ant larvae. Once the elaiosome is consumed the seed is usually discarded in underground middens or ejected from the nest. Although diaspores are seldom distributed far from the parent plant, myrmecochores also benefit from this predominantly mutualistic interaction through dispersal to favourable locations for germination as well as escape from seed predation.

Afzelia africana seeds bearing elaiosomes.

Distribution and diversity

Myrmecochory is exhibited by more than 3,000 plant species worldwide  and is present in every major biome on all continents except Antarctica. Seed dispersal by ants is particularly common in the dry heat and sclerophyll woodlands of Australia (1,500 species) and the South African fynbos (1,000 species). Both regions have a Mediterranean climate and largely infertile soils (characterized by low phosphorus availability), two factors that are often cited to explain the distribution of myrmecochory . Myrmecochory is also present in mesic forests in temperate regions of the northern hemisphere (i.e. in Europe and in eastern North Ameria) as well as in tropical forests and dry deserts, though to a lesser degree . Estimates for the true biodiversity of myrmecochorous plants range from 11,000 to as high as 23,000 species worldwide, or approximately 5% of all flowering plant species.

Chelidonium majus diaspores consisting of hard-coated seeds and attached elaiosomes

Evolutionary history

Myrmecochory has evolved independently many times in a large number of plant families. A recent phylogenetic study identified more than 100 separate origins of myrmecochory in 55 families of flowering plants. With many independent evolutionary origins, elaiosomes have evolved from a wide variety of parent tissues. Strong selective pressure or the relative ease with which elaiosomes can develop from parent tissues may explain the multiple origins of myrmecochory. These findings identify myrmecochory as a prime example of convergent evolution. In addition, phylogenetic comparison of myrmecochorous plant groups reveal that more than half of the lineages in which myrmecochory evolved are more species rich than their non-myrmecochorous sister groups. Not only is myrmecochory a convergent trait, but it also promotes diversification in multiple flowering plants lineages.

Ecology

Myrmecochory is usually classified as a mutualism but this is contingent on the degree to which participating species benefit from the interaction. It is likely that several different factors combine to create mutualistic conditions. Myrmecochorous plants may derive benefit from increased dispersal distance, directed dispersal to nutrient-enriched or protected microsites, and/or seed predator avoidance . Costs incurred by myrmecochorous plants include the energy required to provision diaspores, particularly when there is a disproportionate investment of growth-limiting mineral nutrients. For instance, some Australian Acacias invest a significant portion of their yearly phosphorus uptake in producing diaspores. Diaspores must also be protected from outright predation by ants. This is typically accomplished by the production of a hard, smooth testa, or seed coat.

Few studies have examined the costs and benefits to ants participating in myrmecochory. Much remains to be understood about the selective advantages conferred upon myrmecochorous ants. 

No single hypothesis explains the evolution and persistence of myrmecochory. Instead, it is likely that a combination of beneficial effects working at different spatio-temporal scales contribute to the viability of this predominantly mutualistic interaction. Three commonly cited advantages to myrmecochorous plants are increased dispersal distance, directed dispersal, and seed predator avoidance.

Dispersal distance

Increasing dispersal distance from the parent plant is likely to reduce seed mortality resulting from density-dependent effects. Ants can transport seeds as far as 180 m but the average is less than 2 m, and values between 0.5 to 1.5 m are most common. Perhaps due to the relatively limited distance that ants disperse seeds, many myrmecochores exhibit diplochory: a two-staged dispersal mechanism, often with ballistic projection as the initial mechanism, that can increase dispersal distance by as much as 50% . In some cases, ballistic dispersal distance regularly exceeds that of transport by ants. The dispersal distance achieved through myrmecochory is likely to provide an advantage proportionate to the spatial scale of density-dependent effects acting on individual plants. As such, the relatively modest distances ants transport seeds are likely to be more advantageous for myrmecochorous shrubs, forbs and other plants of small stature.

Directed dispersal

Myrmecochorous plants may benefit when ants disperse seeds to nutrient-rich or protected microsites  that enhance germination and establishment of seedlings. Ants disperse seeds in fairly predictable ways, either by disposing of them in underground middens or by ejecting them from the nest. These patterns of ant dispersal are predictable enough to permit plants to manipulate animal behaviour and influence seed fate, effectively directing the dispersal of seeds to desirable sites. For example, myrmecochores can influence seed fate by producing rounder, smoother diaspores that inhibit ants from re-dispersing seeds after elaiosome removal. This increases the likelihood that seeds will remain underground instead of being ejected from the nest.

Nest chemistry is ideally suited for seed germination given that ant colonies are typically enriched with plant nutrients such as phosphorus and nitrate. This is likely to be advantageous in areas with infertile soils and less important in areas with more favourable soil chemistry, as in fertile forests. In fire-prone areas, depth of burial is an important factor for successful post-burn germination. This, in turn, is influenced by the nesting habits of the myrmecochorous ants. As such, the value of directed dispersal is largely context dependent.

Seeds predator avoidance

Myrmecochorous plants escape or avoid seed predation by granivores when ants remove and sequester diaspores. This benefit is particularly pronounced in areas where myrmecochorous plants are subject to heavy seed predation, which may be common. In mesic forest habitats seed predators remove approximately 60% of all dispersed seeds within a few days and eventually remove all seeds not removed by ants. In addition to attracting ants, elaiosomes also appeal to granivores, and their presence can increase seed predation rates.

Nature of the interaction

Myrmecochory is traditionally thought to be a diffuse or facultative mutualism with low specificity between myrmecochores and individual ant species. This assertion has been challenged in a study of Iberian myrmecochores demonstrating the disproportionate importance of specific ant species in dispersing seeds. Ant-plant interactions with a single species of myrmecochore were recorded for 37 species of ant but only 2 of these were found to disperse diaspores to any significant degree; the rest were seed predators or “cheaters” opportunistically feeding on elaiosomes in situ without dispersing seeds. Larger diaspores are hypothesized to increase the degree of specialization since ant mutualists need to be larger to successfully carry the diaspore back to the nest.

Ants, however, do not appear to form obligate relationships with myrmecochorous plants. Since no known ant species relies entirely on elaiosomes for their nutritional needs, ants remain generalist foragers even when entering into relationships with a more specialized myrmecochore.

As with many other facultative mutualisms, cheating is present on both sides of the interaction. Ants cheat by consuming elaiosomes without transporting seeds or through outright seed predation. Myrmecochorous plants can also cheat, either by producing diaspores with non-removable elaiosomes or by simulating the presence of a non-existent reward with chemical cues. Ants are sometimes capable of discriminating between cheaters and mutualists as shown by studies demonstrating preference for the diaspores of non-cheating myrmecochores. Cheating is also inhibited by ecological interactions external to the myrmecochorous interaction; simple models suggest that predation exerts a stabilizing influence on a mutualism such as myrmecochory.

Myrmecochory and invasive species

Myrmecochores are threatened by invasive species in some ecosystems. For instance, the Argentine ant is an aggressive invader capable of displacing native ant populations. Since Argentine ants do not disperse seeds, invasions may lead to a breakdown in the myrmecochory mutualism, inhibiting the dispersal ability of myrmecochores and causing long-term alterations in plant community dynamics. Invasive ant species can also maintain or even increase seed dispersal in their introduced range, as is the case with the red fire ant in the southeastern United States.

Myrmecochorous plants are also capable of invading ecosystems. These invaders may gain an advantage in areas where native ants disperse invasive seeds. Similarly, the spread of myrmecochorous invaders may be inhibited by limitations in the ranges of native ant populations.

References

1. ^ Daniel Simberloff, Marcel Rejmánek, ed. (2010). Encyclopedia of biological invasions. Berkeley: University of California Press. p. 730. ISBN 9780520948433.
2. ^ ^{a b c d e f g h} Beattie, A.J. (1985). The Evolutionary Ecology of Ant-Plant Mutualisms. Cambridge University Press, Cambridge U.K.
3. ^ Beattie, A.J. and Hughes, L. (2002). “Ant–plant interactions” In Plant–Animal Interactions and Evolutionary Approach, (eds C. M. Herrera & O. Pellmyr), pp. 211–35. Blackwell Science, Oxford.
4. ^ ^a b c d Lengyel S, Aaron D. Gove, Andrew M. Latimer, Jonathan D. Majer, Robert R. Dunn (2010). "Convergent evolution of seed dispersal by ants, and phylogeny and biogeography in flowering plants: a global survey"  (PDF). Perspectives in Plant Ecology, Evolution and Systematics 12 (1): 43–55. doi:10.1016/j.ppees.2009.08.001.
5. ^ Westoby, Mark, L. Hughes, and B.L. Rice (1991). “Seed dispersal by ants; comparing infertile with fertile soils.” In Ant-plant interactions, Camilla R. Huxley and David F. Cutler (eds.), pp. 434-447, Oxford University Press, New York.
6. ^ ^a b c d Buckley, R.C. (1982). “Ant-plant interactions: a world review” In Ant-plant interactions in Australia, Buckley R.C. (ed.), pp. 111-141, Dr W. Junk Publishers, The Hague.
7. ^ ^a b c Lengyel S, Aaron D. Gove, Andrew M. Latimer, Jonathan D. Majer, Robert R. Dunn (2009). Chave, Jerome, ed. "Ants Sow the Seeds of Global Diversification in Flowering Plants". PLoS ONE 4 (5): e5480. doi:10.1371/journal.pone.0005480 PMC 2674952. PMID 19436714.
8. ^ Westoby, Mark., Barbara Rice, Julia M. Shelley, David Haig, and J.L. Kohen (1982). “Plants' use of ants for dispersal at West Head, New South Wales” In Ant-plant interactions in Australia, Buckley R.C. (ed.), pp. 75-87, Dr W. Junk Publishers, The Hague.
9. ^ ^{a b c d e} Giladi, Itamar (2006). "Choosing benefits or partners: a review of the evidence for the evolution of myrmecochory". Oikos 112 (3): 481–492. doi:10.1111/j.0030-1299.2006.14258.x.
10. ^ Janzen, D.H. (1970). Herbivores and the number of tree species in tropical forests. The American Naturalist, 104: 501-528.

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METAPOPULATION

A metapopulation consists of a group of spatially separated populations of the same species which interact at some level. The term metapopulation was coined by Richard Levins in 1969 to describe a model of population dynamics of insect pests in agricultural fields, but the idea has been most broadly applied to species in naturally or artificially fragmented habitats. In Levins' own words, it consists of "a population of populations".

A metapopulation is generally considered to consist of several distinct populations together with areas of suitable habitat which are currently unoccupied. In classical metapopulation theory, each population cycles in relative independence of the other populations and eventually goes extinct as a consequence of demographic stochasticity (fluctuations in population size due to random demographic events); the smaller the population, the more chances of inbreeding depression and prone to extinction.

Although individual populations have finite life-spans, the metapopulation as a whole is often stable because immigrants from one population (which may, for example, be experiencing a population boom) are likely to re-colonize habitat which has been left open by the extinction of another population. They may also emigrate to a small population and rescue that population from extinction (called the rescue effect). Such a rescue effect may occur because declining populations leave niche opportunities open to the "rescuers".

The development of metapopulation theory, in conjunction with the development of source-sink dynamics, emphasised the importance of connectivity between seemingly isolated populations. Although no single population may be able to guarantee the long-term survival of a given species, the combined effect of many populations may be able to do this.

Metapopulation theory was first developed for terrestrial ecosystems, and subsequently applied to the marine realm. In fisheries science, the term "sub-population" is equivalent to the metapopulation science term "local population". Most marine examples are provided by relatively sedentary species occupying discrete patches of habitat, with both local recruitment and recruitment from other local populations in the larger metapopulation. Kritzer & Sale have argued against strict application of the metapopulation definitional criteria that extinction risks to local populations must be non-negligible.:³²

An important contributor to metapopulation theory is the Finnish biologist, Ilkka Hanski of the University of Helsinki.

Predation and oscillations

The first experiments with predation and spatial heterogeneity were conducted by G.F. Gause in the 1930s, based on the Lotka-Volterra equation, which was formulated in the mid-1920s, but no further application had been conducted. The Lotka-Volterra equation suggested that the relationship between predators and their prey would result in population oscillations over time based on the initial densities of predator and prey. Gause's early experiments to prove the predicted oscillations of this theory failed because the predator-prey interactions were not influenced by immigration. However, once immigration was introduced, the population cycles accurately depicted the oscillations predicted by the Lotka-Volterra equation, with the peaks in prey abundance shifted slightly to the left of the peaks of the predator densities. Huffaker's experiments expanded on those of Gause by examining how both the factors of migration and spatial heterogeneity lead to predator-prey oscillations.

Huffaker's experiments on predator-prey interactions (1958)

In order to study predation and population oscillations, Huffaker used mite species, one being the predator and the other being the prey. He set up a controlled experiment using oranges, which the prey fed on, as the spatially structured habitat in which the predator and prey would interact. At first, Huffaker experienced difficulties similar to those of Gause in creating a stable predator-prey interaction. By using oranges only, the prey species quickly went extinct followed consequently with predator extinction. However, he discovered that by modifying the spatial structure of the habitat, he could manipulate the population dynamics and allow the overall survival rate for both species to increase. He did this by altering the distance between the prey and oranges (their food), establishing barriers to predator movement, and creating corridors for the prey to disperse. These changes resulted in increased habitat patches and in turn provided more areas for the prey to seek temporary protection. When the prey would go extinct locally at one habitat patch, they were able to reestablish by migrating to new patches before being attacked by predators. This habitat spatial structure of patches allowed for coexistence between the predator and prey species and promoted a stable population oscillation model. Although the term metapopulation had not yet been coined, the environmental factors of spatial heterogeneity and habitat patchiness would later describe the conditions of a metapopulation relating to how groups of spatially separated populations of species interact with one another. Huffaker's experiment is significant because it showed how metapopulations can directly affect the predator-prey interactions and in turn influence population dynamics.

The Levins model

Levins' original model applied to a metapopulation distributed over many patches of suitable habitat with significantly less interaction between patches than within a patch. Population dynamics within a patch were simplified to the point where only presence and absence were considered. Each patch in his model is either populated or not.

Let N be the fraction of patches occupied at a given time. During a time dt, each occupied patch can become unoccupied with an extinction probability edt. Additionally, 1 − N of the patches are unoccupied. Assuming a constant rate c of propagule, generation from each of the N occupied patches, during a time dt, each unoccupied patch can become occupied with a colonization probability cNdt . Accordingly, the time rate of change of occupied patches, dN/dt, is

This equation is mathematically equivalent to the logistic model, with a carrying capacity Kgiven by

and growth rate r

At equilibrium, therefore, some fraction of the species's habitat will always be unoccupied.

Stochasticity and metapopulations

Huffaker's  studies of spatial structure and species interactions are an example of early experimentation in metapopulation dynamics. Since the experiments of Huffaker  and Levins, models have been created which integrate stochastic factors. These models have proven that the combination of environmental variability (stochasticity) and relatively small migration rates cause indefinite or unpredictable persistence. However, Huffaker's experiment almost guaranteed infinite persistence because of the controlled immigration variable.

Stochastic patch occupancy models (SPOMs)

One major drawback of the Levins model is that it is deterministic, whereas the fundamental metapopulation processes are stochastic. Metapopulations are particularly useful when discussing species in disturbed habitats and the viability of their populations i.e., how likely they are to become extinct in a given time interval. The Levins model cannot address this issue. A simple way to extend the Levins' model to incorporate space and stochastic considerations is by using the contact process (mathematics). Simple modifications to this model can also incorporate for patch dynamics. At a given percolation threshold, habitat fragmentation effects take place in these configuration predicting more drastic extinction thresholds. 

For conservation biology purposes, metapopulation models must include (a) the finite nature of metapopulations (how many patches are suitable for habitat), and (b) the probabilistic nature of extinction and colonisation. Also, note that in order to apply these models, the extinctions and colonisations of the patches must be asynchronous.

Microhabitat patches (MHPs) and bacterial metapopulations

E. coli metapopulation on-chip.

By combining nanotechnology with landscape ecology, a habitat landscape can be nanofabricated on-chip by building a collection of nanofabricated bacterial habitats, and connecting them by corridors in different topological arrangements and with nano-scale channels providing them with the local ecosystem service of habitat renewal. These landscapes of MHPs can be used as physical implementations of an adaptive landscape: by generating a spatial mosaic of patches of opportunity distributed in space and time. The patchy nature of these fluidic landscapes allows for the study of adapting bacterial cells in a metapopulation system operating on-chip within a synthetic ecosystem. The metapopulation biology and evolutionary ecology of these bacterial systems, in these synthetic ecosystems, can be addressed using experimental biophysics.

Life history evolution

Metapopulation models have been used to explain life-history evolution, such as the ecological stability of amphibian metamorphosis in small vernal ponds. Alternative ecological strategies have evolved. For example, some salamanders forgo metamorphosis and sexually mature as aquatic neotenes. The seasonal duration of wetlands and the migratory range of the species determines which ponds are connected and if they form a metapopulation. The duration of the life history stages of amphibians relative to the duration of the vernal pool before it dries up regulates the ecological development of metapopulations connecting aquatic patches to terrestrial patches.

References

1. ^ ^a b Levins, R. (1969), "Some demographic and genetic consequences of environmental heterogeneity for biological control", Bulletin of the Entomological Society of America 15: 237–240
2. ^ ^a b Kritzer, JP & Sale, PF (eds) (2006) Marine metapopulations, Academic Press, New York.
3. ^ ^a b Real, Leslie A. and Brown, James H. 1991. Foundations of Ecology: Classic papers with commentaries. The University of Chicago Press, Chicago.
4. ^ ^a b c Huffaker, C.B. (1958), "Experimental Studies on Predation: Dispersion factors and predator-prey oscillations", Hilgardia 27 (343): 83
5. ^ Legendre, P.; Fortin, M.J. (1989), "Spatial pattern and ecological analysis", Plant Ecology 80 (2): 107, doi:10.1007/BF00048036.
6. ^ Kareiva, P. (1987), "Habitat Fragmentation and the Stability of Predator-Prey Interactions", Nature 326 (6111): 388, Bibcode:1987Natur.326..388K, doi:10.1038/326388a0.
7. ^ Janssen, A. et al. 1997. Metapopulation Dynamics of a Persisting Predator-Prey system.
8. ^ Keymer J.E, P.A. Marquet, J.X. Velasco‐Hernández, S.A. Levin (November 2000). "Extinction Thresholds and Metapopulation Persistence in Dynamic Landscapes". The American Naturalist 156: 478–4945. doi:10.1086/303407.
9. ^ Keymer J.E., P. Galajda, C. Muldoon R., and R. Austin (November 2006). "Bacterial metapopulations in nanofabricated landscapes". PNAS 103 (46): 17290–295. Bibcode: 2006PNAS..10317290K. doi:10.1073/pnas.0607971103. PMC 1635019. PMID 17090676.
10. ^ Petranka, J. W. (2007), "Evolution of complex life cycles of amphibians: bridging the gap between metapopulation dynamics and life history evolution", Evolutionary Ecology 21(6): 751–764, doi:10.1007/s10682-006-9149-1.

• Bascompte J., Solé R. V. (1996), "Habitat Fragmentation and Extinction Thresholds in spatially explicit models", Journal of Animal Ecology 65 (4): 465–473, doi:10.2307/5781.
• Hanski, I. Metapopulation Ecology Oxford University Press. 1999. ISBN 0-19-854065-5.
• Fahrig, L. 2003. Effects of Habitat Fragmentation on Biodiversity. Annual Review of ecology, evolution, and systematics. 34:1, p. 487.
• Levin S.A. (1974), "Dispersion and Population Interactions", The American Naturalist 108(960): 207, doi:10.1086/282900.

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