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".[1]

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.[2] 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.[2]:32

Finnish biologist Ilkka Hanski of the University of Helsinki was an important contributor to metapopulation theory.

Metapopulation (1)
Metapopulations are important in fisheries. The local population (1.) serves as a source for hybridization with surrounding subspecies populations (1.a, 1.b, and 1.c).The populations are normally spatially separated and independent but spatial overlap during breeding times allows for gene flow between the populations.

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.[3] 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.[4] 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.[5] 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 became 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.[3] These changes resulted in increased habitat patches and in turn provided more areas for the prey to seek temporary protection. When the prey would become 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.[6] 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.[7]

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 K given by

and growth rate r

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

Stochasticity and metapopulations

Huffaker's[4] studies of spatial structure and species interactions are an example of early experimentation in metapopulation dynamics. Since the experiments of Huffaker[4] and Levins,[1] models have been created which integrate stochastic factors. These models have shown 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. Simple modifications to this model can also incorporate for patch dynamics. At a given percolation threshold, habitat fragmentation effects take place in these configurations predicting more drastic extinction thresholds.[8]

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

Ecoli metapopulation
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:[9] 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.[10]

See also


  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 (3): 237–240, doi:10.1093/besa/15.3.237
  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): 343–383, doi:10.3733/hilg.v27n14p343
  5. ^ Legendre, P.; Fortin, M.J. (1989), "Spatial pattern and ecological analysis", Plant Ecology, 80 (2): 107, CiteSeerX, doi:10.1007/BF00048036
  6. ^ Kareiva, P. (1987), "Habitat Fragmentation and the Stability of Predator–Prey Interactions", Nature, 326 (6111): 388–390, 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 (5): 478–4945. doi:10.1086/303407. PMID 29587508.
  9. ^ Keymer J.E.; P. Galajda; C. Muldoon R. & 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, JSTOR 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.

External links

Ecological facilitation

Ecological facilitation or probiosis describes species interactions that benefit at least one of the participants and cause harm to neither. Facilitations can be categorized as mutualisms, in which both species benefit, or commensalisms, in which one species benefits and the other is unaffected. Much of classic ecological theory (e.g., natural selection, niche separation, metapopulation dynamics) has focused on negative interactions such as predation and competition, but positive interactions (facilitation) are receiving increasing focus in ecological research. This article addresses both the mechanisms of facilitation and the increasing information available concerning the impacts of facilitation on community ecology.

Ecology (disciplines)

Ecology is a broad biological science and can be divided into many sub-disciplines using various criteria. Many of these fields overlap, complement and inform each other, and few of these disciplines exist in isolation. For example, the population ecology of an organism is a consequence of its behavioral ecology and intimately tied to its community ecology. Methods from molecular ecology might inform the study of the population, and all kinds of data are modeled and analyzed using quantitative ecology techniques.

When discussing the study of a single species, a distinction is usually made between its biology and its ecology. For example, "polar bear biology" might include the study of the polar bear's physiology, morphology, pathology and ontogeny, whereas "polar bear ecology" would include a study of its prey species, its population and metapopulation status, distribution, dependence on environmental conditions, etc. In that sense, there can be as many subdisciplines of ecology as there are species to study.

Extinction debt

In ecology, extinction debt is the future extinction of species due to events in the past. The phrases dead clade walking and survival without recovery express the same idea.Extinction debt occurs because of time delays between impacts on a species, such as destruction of habitat, and the species' ultimate disappearance. For instance, long-lived trees may survive for many years even after reproduction of new trees has become impossible, and thus they may be committed to extinction. Technically, extinction debt generally refers to the number of species in an area likely to become extinct, rather than the prospects of any one species, but colloquially it refers to any occurrence of delayed extinction.

Extinction debt may be local or global, but most examples are local as these are easier to observe and model. It is most likely to be found in long-lived species and species with very specific habitat requirements (specialists). Extinction debt has important implications for conservation, as it implies that species may become extinct due to past habitat destruction, even if continued impacts cease, and that current reserves may not be sufficient to maintain the species that occupy them. Interventions such as habitat restoration may reverse extinction debt.

Immigration credit is the corollary to extinction debt. It refers to the number of species likely to migrate to an area after an event such as the restoration of an ecosystem.

Extinction threshold

Extinction threshold is a term used in conservation biology to explain the point at which a species, population or metapopulation, experiences an abrupt change in density or number because of an important parameter, such as habitat loss. It is at this critical value below which a species, population, or metapopulation, will go extinct, though this may take a long time for species just below the critical value, a phenomenon known as extinction debt.Extinction thresholds are important to conservation biologists when studying a species in a population or metapopulation context because the colonization rate must be larger than the extinction rate, otherwise the entire entity will go extinct once it reaches the threshold.Extinction thresholds are realized under a number of circumstances and the point in modeling them is to define the conditions that lead a population to extinction. Modeling extinction thresholds can explain the relationship between extinction threshold and habitat loss and habitat fragmentation.

Gap analysis (conservation)

Gap analysis is a tool used in wildlife conservation to identify gaps in conservation lands (e.g., protected areas and nature reserves) or other wildlands where significant plant and animal species and their habitat or important ecological features occur.Conservation managers or scientists can use it as a basis for providing recommendations to improve the representativeness of nature reserves or the effectiveness of protected areas so that these areas provide the best value for conserving biological diversity. With the information that a gap analysis yields, the boundaries of protected areas may be designed to subsume 'gaps' containing significant populations of wildlife species that can enhance the long-term survival of a larger metapopulation of the species already within the managed or protected area, or to include a diversity of wildlife species or ecosystems that merit protection but are inadequately represented in an existing protected area network. Gap assessments can be done using the geographic information system: land maps that delineate topography, biological and geological features (forest cover, plains, rivers, etc.), boundaries, land ownership and use are overlaid with the distribution of wildlife species. How much of the species' distribution fall within or without the conservation lands, or within a highly exploited area etc. can be identified.

At its simplest, a gap analysis is an assessment of the extent to which a protected area system meets protection goals set by a nation or region to represent its biological diversity. Gap analyses can vary from simple exercises based on a spatial comparison of biodiversity with existing protected areas to complex studies that need detailed data gathering and analysis, mapping and use of software decision packages.

Ilkka Hanski

Ilkka Aulis Hanski (14 February 1953 – 10 May 2016) was a Finnish ecologist at the University of Helsinki, Finland. The Metapopulation Research Center led by Hanski, until his death, has been nominated as a Center of Excellence by the Academy of Finland. The group studies species living in fragmented landscapes and attempts to advance metapopulation ecology research. Metapopulation ecology itself studies populations of plants and animals which are separated in space by occupying patches.

Marsh fritillary

The marsh fritillary (Euphydryas aurinia) is a butterfly of the family Nymphalidae. Commonly distributed in the Palearctic region, the marsh fritillary got its common name due to its habitat: marshy, damp wetlands and grasslands. The prolonged larval stage lasts for approximately seven to eight months and includes a period of hibernation over the winter. The larvae are dependent on the host food plant Succisa pratensis not only for feeding but also for hibernation, because silken webs are formed on the host plant as the gregarious larvae enter hibernation. Because female butterflies lay eggs in batches on the host plant, females are selective about the location of oviposition. The cost of laying the batches of eggs at an unfavorable location is high and extensive.Over the past few decades, the E. aurinia population has faced rapid decline and become endangered as a consequence of landscape and climate changes. Loss of habitat due to decline of host plant population has been the biggest factor. Although efforts of conservation and management have revived the population slightly, the metapopulations of E. aurinia are still vulnerable to extinction.


An ecological metacommunity is a set of interacting communities which are linked by the dispersal of multiple, potentially interacting species. The term is derived from the field of community ecology, which is primarily concerned with patterns of species distribution, abundance and interactions. Metacommunity ecology combines the importance of local factors (environmental conditions, competition, predation) and regional factors (dispersal of individuals, immigration, emigration) to explain patterns of species distributions that happen in different spatial scales.

There are four theoretical frameworks, or unifying themes, that each detail specific mechanistic processes useful for predicting empirical community patterns. These are the patch dynamics, species sorting, source–sink dynamics (or mass effect) and neutral model frameworks. Patch dynamics models describe species composition among multiple, identical patches, such as islands. In this framework, species are able to persist on patches through tradeoffs in colonization ability and competitive ability, where less competitive species can disperse to unoccupied patches faster than they go extinct in others. Species sorting models describe variation in abundance and composition within the metacommunity due to individual species responses to environmental heterogeneity, such that certain local conditions may favor certain species and not others. Under this perspective, species are able to persist in patches with suitable environmental conditions resulting in a strong correlation between local species composition and the environment. This model represents the classical theories of the niche-centric era of G. Evelyn Hutchinson and Robert MacArthur. Source-sink models describe a framework in which dispersal and environmental heterogeneity interact to determine local and regional abundance and composition. This framework is derived from the metapopulation ecology term describing source–sink dynamics at the population level. High levels of dispersal among habitat patches allows populations to be maintained in environments that are normally outside the species environmental range. Finally, the neutral perspective describes a framework where species are essentially equivalent in their competitive and dispersal abilities, and local and regional composition and abundance is determined primarily by stochastic demographic processes and dispersal limitation. The neutral perspective was recently popularized by Stephen Hubbell following his groundbreaking work on the unified neutral theory of biodiversity.

Molecular ecology

Molecular ecology is a field of evolutionary biology that is concerned with applying molecular population genetics, molecular phylogenetics, and more recently genomics to traditional ecological questions (e.g., species diagnosis, conservation and assessment of biodiversity, species-area relationships, and many questions in behavioral ecology). It is virtually synonymous with the field of "Ecological Genetics" as pioneered by Theodosius Dobzhansky, E. B. Ford, Godfrey M. Hewitt and others. These fields are united in their attempt to study genetic-based questions "out in the field" as opposed to the laboratory. Molecular ecology is related to the field of Conservation genetics.

Methods frequently include using microsatellites to determine gene flow and hybridization between populations. The development of molecular ecology is also closely related to the use of DNA microarrays, which allows for the simultaneous analysis of the expression of thousands of different genes. Quantitative PCR may also be used to analyze gene expression as a result of changes in environmental conditions or different response by differently adapted individuals.

Neonympha mitchellii

Neonympha mitchellii is an endangered species of nymphalid butterfly of the eastern United States. There are two known subspecies:

N. m. mitchellii, the nominate subspecies, commonly called Mitchell's satyr or Mitchell's marsh satyr, is found in Michigan and Indiana. The species is presumably extirpated from former ranges in Ohio (last seen in the 1950s), New Jersey (last seen in 1988), and Wisconsin.

N. m. francisci, commonly called Saint Francis' satyr, is found in a single metapopulation in a 10×10 km area of Fort Bragg in North Carolina.Recent discoveries since 1998 of populations in Alabama, Mississippi, and Virginia are being studied for taxonomic classification, and may be grouped with N. m. mitchellii or be described as new subspecies.All subspecies, including those newly discovered, are federally protected under the Endangered Species Act.Its larvae can feed upon the highly-invasive Japanese stilt grass Microstegium vimineum, so populations of this butterfly are potentially at risk from efforts to remove stilt grass. A butterfly of similar appearance, the Carolina Satyr Hermeuptychia sosybius, is also able to feed upon stilt grass.

Occupancy frequency distribution

In macroecology and community ecology, an occupancy frequency distribution (OFD) is the distribution of the numbers of species occupying different numbers of areas. It was first reported in 1918 by the Danish botanist Christen C. Raunkiær in his study on plant communities. The OFD is also known as the species-range size distribution in literature.

Occupancy–abundance relationship

In ecology, the occupancy–abundance (O–A) relationship is the relationship between the abundance of species and the size of their ranges within a region. This relationship is perhaps one of the most well-documented relationships in macroecology, and applies both intra- and interspecifically (within and among species). In most cases, the O–A relationship is a positive relationship. Although an O–A relationship would be expected, given that a species colonizing a region must pass through the origin (zero abundance, zero occupancy) and could reach some theoretical maximum abundance and distribution (that is, occupancy and abundance can be expected to co-vary), the relationship described here is somewhat more substantial, in that observed changes in range are associated with greater-than-proportional changes in abundance. Although this relationship appears to be pervasive (e.g. Gaston 1996 and references therein), and has important implications for the conservation of endangered species, the mechanism(s) underlying it remain poorly understood

Population dynamics of fisheries

A fishery is an area with an associated fish or aquatic population which is harvested for its commercial or recreational value. Fisheries can be wild or farmed. Population dynamics describes the ways in which a given population grows and shrinks over time, as controlled by birth, death, and migration. It is the basis for understanding changing fishery patterns and issues such as habitat destruction, predation and optimal harvesting rates. The population dynamics of fisheries is used by fisheries scientists to determine sustainable yields.The basic accounting relation for population dynamics is the BIDE (Birth, Immigration, Death, Emigration) model, shown as:

N1 = N0 + B − D + I − Ewhere N1 is the number of individuals at time 1, N0 is the number of individuals at time 0, B is the number of individuals born, D the number that died, I the number that immigrated, and E the number that emigrated between time 0 and time 1. While immigration and emigration can be present in wild fisheries, they are usually not measured.

A fishery population is affected by three dynamic rate functions:

Birth rate or recruitment. Recruitment means reaching a certain size or reproductive stage. With fisheries, recruitment usually refers to the age a fish can be caught and counted in nets.

Growth rate. This measures the growth of individuals in size and length. This is important in fisheries where the population is often measured in terms of biomass.

Mortality. This includes harvest mortality and natural mortality. Natural mortality includes non-human predation, disease and old age.If these rates are measured over different time intervals, the harvestable surplus of a fishery can be determined. The harvestable surplus is the number of individuals that can be harvested from the population without affecting long term stability (average population size). The harvest within the harvestable surplus is called compensatory mortality, where the harvest deaths are substituting for the deaths that would otherwise occur naturally. Harvest beyond that is additive mortality, harvest in addition to all the animals that would have died naturally.

Care is needed when applying population dynamics to real world fisheries. Over-simplistic modelling of fisheries has resulted in the collapse of key stocks.

Population ecology

Population ecology is a sub-field of ecology that deals with the dynamics of species populations and how these populations interact with the environment. It is the study of how the population sizes of species change over time and space. The term population ecology is often used interchangeably with population biology or population dynamics.

The development of population ecology owes much to demography and actuarial life tables. Population ecology is important in conservation biology, especially in the development of population viability analysis (PVA) which makes it possible to predict the long-term probability of a species persisting in a given habitat patch. Although population ecology is a subfield of biology, it provides interesting problems for mathematicians and statisticians who work in population dynamics.


A Psilon is a unit of length that is equal to 44 manly strides or, more precisely, 0.025 miles (a quarter of a tenth of a mile) or 40 meters. The Psilon is best known as the official unit of measurement during the censusing of plants in the genus Silene (see Silene) in a metapopulation surrounding Mountain Lake Biological Station in Giles County, Virginia.

Etymology: Derived from the Latin Silene.

Richard Levins

Richard "Dick" Levins (June 1, 1930 – January 19, 2016) was an ex-tropical farmer turned ecologist, a population geneticist, biomathematician, mathematical ecologist, and philosopher of science who had researched diversity in human populations. Until his death, Levins was a university professor at the Harvard T.H. Chan School of Public Health and a long-time political activist. He was best known for his work on evolution and complexity in changing environments and on metapopulations.

Levins' writing and speaking is extremely condensed. This, combined with his Marxism, has made his analyses less well-known than those of some other ecologists and evolutionists who were adept at popularization. One story of his Chicago years is that, in order to understand his lectures, his graduate students each needed to attend Levins' courses three times: the first time to acclimate themselves to the speed of his delivery and the difficulty of his mathematics; the second to get the basic ideas down; and the third to pick up his subtleties and profundities.Levins also had written on philosophical issues in biology and modelling. One of his influential articles is "The Strategy of Model Building in Population Biology". He has influenced a number of contemporary philosophers of biology. Levins often boasted publicly that he was a 'fourth generation Marxist' and often had said that the methodology in his Evolution in Changing Environments was based upon the introduction to Marx's Grundrisse, the rough draft of Das Kapital. With the evolutionary geneticist Richard Lewontin, Levins had written a number of articles on methodology, philosophy, and social implications of biology. Many of these are collected in The Dialectical Biologist. In 2007, the duo published a second thematic collection of essays titled Biology Under the Influence: Dialectical Essays on Ecology, Agriculture, and Health.Also with Lewontin, Levins had co-authored a number of satirical articles criticizing sociobiology, systems modeling in ecology, and other topics under the pseudonym Isadore Nabi. Levins and Lewontin managed to place a ridiculous biography of Nabi and his achievements in American Men of Science, thereby showing how little editorial care and fact-checking work went on in that respected reference work.

SLOSS debate

The SLOSS debate was a debate in ecology and conservation biology during the 1970s and 1980s as to whether a single large or several small (SLOSS) reserves were a superior means of conserving biodiversity in a fragmented habitat.

In 1975, Jared Diamond suggested some "rules" for the design of protected areas, based on Robert MacArthur and E. O. Wilson's book The Theory of Island Biogeography. One of his suggestions was that a single large reserve was preferable to several smaller reserves whose total areas were equal to the larger.

Since species richness increases with habitat area, a larger block of habitat would support more species than any of the smaller blocks. This idea was popularised by many other ecologists, and has been incorporated into most standard textbooks in conservation biology, and was used in real-world conservation planning. This idea was challenged by Wilson's former student Daniel Simberloff, who pointed out that this idea relied on the assumption that smaller reserves had a nested species composition — it assumed that each larger reserve had all the species presented in any smaller reserve. If the smaller reserves had unshared species, then it was possible that two smaller reserves could have more species than a single large reserve.

Simberloff and Abele expanded their argument in subsequent paper in the journal The American Naturalist stating neither ecological theory nor empirical data exist to support the hypothesis that subdividing a nature reserve would increase extinction rates, basically negating Diamond as well as MacArthur and Wilson. Bruce A. Wilcox and Dennis D. Murphy responded with a key paper "Conservation strategy - effects of fragmentation on extinction" pointing out flaws in their argument while providing a comprehensive definition of habitat fragmentation. Wilcox and Murphy also argued that habitat fragmentation is probably the major threat to the loss of global biological diversity.

This helped set the stage for fragmentation research as an important area of conservation biology. The SLOSS debate ensued as to the extent to which smaller reserves shared species with one another, leading to the development of nested subset theory by Bruce D. Patterson and Wirt Atmar in the 1980s and to the establishment of the Biological Dynamics of Forest Fragments Project (BDFFP) near Manaus, Brazil in 1979 by Thomas Lovejoy and Richard Bierregaard. In the field of metapopulation ecology, modelling works suggest that the SLOSS debate should be refined and cannot be solved without explicit spatial consideration of dispersal and environmental dynamics. In particular, a large number of small patches may be optimal to long-term species persistence only if the species range increases with the number of patches.In conservation biology and conservation genetics, metapopulations (i.e. connected groups of sub-populations) are considered to be more stable if they are larger, or have more populations . This is because although individual small populations may go extinct due to stochastic processes of environment or biology (such as genetic drift and inbreeding), they can be recolonized by rare migrants from other surviving populations. Thus several small populations could be better than a single large: if a catastrophe wipes out a single big population, the species goes extinct, but if some regional populations in a large metapopulation get wiped out, recolonization from the rest of the metapopulation can ensure their eventual survival. The original SLOSS debate tended to ignore dispersal and genetics.

Source–sink dynamics

Source–sink dynamics is a theoretical model used by ecologists to describe how variation in habitat quality may affect the population growth or decline of organisms.

Since quality is likely to vary among patches of habitat, it is important to consider how a low quality patch might affect a population. In this model, organisms occupy two patches of habitat. One patch, the source, is a high quality habitat that on average allows the population to increase. The second patch, the sink, is very low quality habitat that, on its own, would not be able to support a population. However, if the excess of individuals produced in the source frequently moves to the sink, the sink population can persist indefinitely. Organisms are generally assumed to be able to distinguish between high and low quality habitat, and to prefer high quality habitat. However, ecological trap theory describes the reasons why organisms may actually prefer sink patches over source patches. Finally, the source-sink model implies that some habitat patches may be more important to the long-term survival of the population, and considering the presence of source-sink dynamics will help inform conservation decisions.

Y Chromosome Haplotype Reference Database

The Y Chromosome Haplotype Reference Database (YHRD) is an open access, annotated collection of population samples typed for Y chromosomal sequence variants. Two important objectives are pursued: (1) the generation of reliable frequency estimates for Y-STR haplotypes and Y-SNP haplotypes to be used in the quantitative assessment of matches in forensic and kinship cases and (2) the characterization of male lineages to draw conclusions about the origins and history of human populations. Since its creation in 1999 it has been curated by Lutz Roewer and Sascha Willuweit at the Institute of Legal Medicine and Forensic Sciences, Charité - Universitätsmedizin Berlin. The database is endorsed by the International Society for Forensic Genetics (ISFG).

By June 2019 285,406 9-STR locus haplotypes, among them 225,098 17-STR locus haplotypes, 62,737 23-STR locus haplotypes, 56,114 27-STR locus haplotypes and 24,328 Y SNP profiles sampled in 135 countries have been directly submitted by forensic institutions and universities from 72 countries. In geographic terms, 47% of the YHRD samples stem from Asia, 23% from Europe, 14% from North America, 11% from Latin America, 3% from Africa, 1% from Oceania/Australia and 0.3% from the Arctic (release 61 of June 24, 2019). The 1,308 individual sampling projects are described in more than 560 peer-reviewed publications

Food webs
Example webs
Ecology: Modelling ecosystems: Other components


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