Population viability analysis

Population viability analysis (PVA) is a species-specific method of risk assessment frequently used in conservation biology. It is traditionally defined as the process that determines the probability that a population will go extinct within a given number of years. More recently, PVA has been described as a marriage of ecology and statistics that brings together species characteristics and environmental variability to forecast population health and extinction risk. Each PVA is individually developed for a target population or species, and consequently, each PVA is unique. The larger goal in mind when conducting a PVA is to ensure that the population of a species is self-sustaining over the long term.[1]


Population viability analysis (PVA) is used to estimate the likelihood of a population’s extinction and indicate the urgency of recovery efforts, and identify key life stages or processes that should be the focus of recovery efforts. PVA is also used to identify factors that drive population dynamics, compare proposed management options and assess existing recovery efforts [2]. PVA is frequently used in endangered species management to develop a plan of action, rank the pros and cons of different management scenarios, and assess the potential impacts of habitat loss.[3]


In the 1970s, Yellowstone National Park was the centre of a heated debate over different proposals to manage the park’s problem grizzly bears (Ursus arctos). In 1978, Mark Shaffer proposed a model for the grizzlies that incorporated random variability, and calculated extinction probabilities and minimum viable population size. The first PVA is credited to Shaffer.

PVA gained popularity in the United States as federal agencies and ecologists required methods to evaluate the risk of extinction and possible outcomes of management decisions, particularly in accordance with the Endangered Species Act of 1973, and the National Forest Management Act of 1976.

In 1986, Gilpin and Soulé broadened the PVA definition to include the interactive forces that affect the viability of a population, including genetics. The use of PVA increased dramatically in the late 1980s and early 1990s following advances in personal computers and software packages.


The endangered Fender's blue butterfly (Icaricia icarioides) was recently assessed with a goal of providing additional information to the United States Fish and Wildlife Service, which was developing a recovery plan for the species. The PVA concluded that the species was more at risk of extinction than previously thought and identified key sites where recovery efforts should be focused. The PVA also indicated that because the butterfly populations fluctuate widely from year to year, to prevent the populations from going extinct the minimum annual population growth rate must be kept much higher than at levels typically considered acceptable for other species.[4]

Following a recent outbreak of canine distemper virus, a PVA was performed for the critically endangered island fox (Urocyon littoralis) of Santa Catalina Island, California. The Santa Catalina island fox population is uniquely composed of two subpopulations that are separated by an isthmus, with the eastern subpopulation at greater risk of extinction than the western subpopulation. PVA was conducted with the goals of 1) evaluating the island fox’s extinction risk, 2) estimating the island fox’s sensitivity to catastrophic events, and 3) evaluating recent recovery efforts which include release of captive-bred foxes and transport of wild juvenile foxes from the west to the east side. Results of the PVA concluded that the island fox is still at significant risk of extinction, and is highly susceptible to catastrophes that occur more than once every 20 years. Furthermore, extinction risks and future population sizes on both sides of the island were significantly dependent on the number of foxes released and transported each year.[5]

PVAs in combination with sensitivity analysis can also be used to identify which vital rates has the relative greatest effect on population growth and other measures of population viability. For example, a study by Manlik et al. (2016) forecast the viability of two bottlenose dolphin populations in Western Australia and identified reproduction as having the greatest influence on the forecast of these populations. One of the two populations was forecast to be stable, whereas the other population was forecast to decline, if it isolated from other populations and low reproductive rates persist. The difference in viability between the two studies was primarily due to differences in reproduction and not survival. The study also showed that temporal variation in reproduction had a greater effect on population growth than temporal variation in survival.[6]


Debates exist and remain unresolved over the appropriate uses of PVA in conservation biology and PVA’s ability to accurately assess extinction risks.

A large quantity of field data is desirable for PVA; some conservatively estimate that for a precise extinction probability assessment extending T years into the future, five-to-ten times T years of data are needed. Datasets of such magnitude are typically unavailable for rare species; it has been estimated that suitable data for PVA is available for only 2% of threatened bird species. PVA for threatened and endangered species is particularly a problem as the predictive power of PVA plummets dramatically with minimal datasets. Ellner et al. (2002) argued that PVA has little value in such circumstances and is best replaced by other methods. Others argue that PVA remains the best tool available for estimations of extinction risk, especially with the use of sensitivity model runs.

Even with an adequate dataset, it is possible that a PVA can still have large errors in extinction rate predictions. It is impossible to incorporate all future possibilities into a PVA: habitats may change, catastrophes may occur, new diseases may be introduced. PVA utility can be enhanced by multiple model runs with varying sets of assumptions including the forecast future date. Some prefer to use PVA always in a relative analysis of benefits of alternative management schemes, such as comparing proposed resource management plans.

Accuracy of PVAs has been tested in a few retrospective studies. For example, a study comparing PVA model forecasts with the actual fate of 21 well-studied taxa, showed that growth rate projections are accurate, if input variables are based on sound data, but highlighted the importance of understanding density-dependence (Brook et al. 2000).[7] Also, McCarthey et al. (2003)[8] showed that PVA predictions are relatively accurate, when they are based on long-term data. Still, the usefulness of PVA lies more in its capacity to identify and assess potential threats, than in making long-term, categorical predictions (Akçakaya & Sjögren-Gulve 2000).[9]

Future directions

Improvements to PVA likely to occur in the near future include: 1) creating a fixed definition of PVA and scientific standards of quality by which all PVA are judged and 2) incorporating recent genetic advances into PVA.

See also


  1. ^ Sanderson, E.W. (2006) How many animals do we want to save? The many ways of setting population target levels for conservation. BioScience 56: 911-922, (p. 913).
  2. ^ Manlik O.; Lacy R.C.; Sherwin W.B. (2018). "Applicability and limitations of sensitivity analyses for wildlife management". Journal of Applied Ecology. 55 (3): 1430–1440. doi:10.1111/1365-2664.13044.
  3. ^ Beissenger S.R.; McCullough D.R., eds. (2002). Population Viability Analysis. Chicago: The University of Chicago Press. ISBN 978-0-226-04178-0.
  4. ^ Schultz, Cheryl B.; Hammond, Paul C. (October 2003). "Using Population Viability Analysis to Develop Recovery Criteria for Endangered Insects: Case Study of the Fender's Blue Butterfly". Conservation Biology. 17 (5): 1372–1385. doi:10.1046/j.1523-1739.2003.02141.x.
  5. ^ Kohlmann, Stephan G.; Schmidt, Gregory A.; Garcelon, David K. (April 2005). "A population viability analysis for the Island Fox on Santa Catalina Island, California". Ecological Modelling. 183 (1): 77–94. doi:10.1016/j.ecolmodel.2004.07.022.
  6. ^ Manlik O.; McDonald J.A.; Mann J.; Raudino H.C.; Bejder L.; Kruetzen M.; Connor R.C.; Heithaus M.R.; Lacy R.C.; Sherwin W.B. (2016). "The relative importance of reproduction and survival for the conservation of two dolphin populations". Ecology and Evolution. 6 (11): 3496–3512. doi:10.1002/ece3.2130.
  7. ^ Brook B.W.; O'Grady J.J.; Chapman A.P.; Burgman H.R.; Akçakaya H.R.; Frankham R. (2000). "Predictive accuracy of population viability analysis in conservation biology". Nature. 329: 512–519.
  8. ^ McCarthy M.A.; Andelman S.J.; Possingham H.P. (2003). "Reliability of relative predictions in population viability analysis" (PDF). Conservation Biology. 17 (4): 982–989. doi:10.1046/j.1523-1739.2003.01570.x.
  9. ^ Akçakaya H.R.; Sjörgren-Gulve P. (2000). "Population viability analysis in conservation planning: an overview". Ecological Bulletins. 48: 9–21.

Further reading

  • Beissinger, Steven R. and McCullough, Dale R. (2002). “Population Viability Analysis”, Chicago: University of Chicago Press.
  • Beissinger, S.R. & Westphal, M.I. (1998). "On the use of demographic models of population viability in endangered species management". Journal of Wildlife Management. 62 (3): 821–841. doi:10.2307/3802534. JSTOR 3802534.
  • Brook, B.W., Burgman, M.A., Akçakaya, H.R., O'Grady, J.J., and Frankham, R. (2002). "Critiques of PVA ask the wrong questions: Throwing the heuristic baby out with the numerical bath water". Conservation Biology. 16: 262–263. doi:10.1046/j.1523-1739.2002.01426.x.CS1 maint: Multiple names: authors list (link)
  • Brook, B.W., J.J. O'Grady, A.P. Chapman, M.A. Burgman, H.R. Akçakaya, and R. Frankham (2000). "Predictive accuracy of population viability analysis in conservation biology". Nature. 404 (6776): 385–387. doi:10.1038/35006050. PMID 10746724.CS1 maint: Multiple names: authors list (link)
  • Crouse, D.T., Crowder, L.B., and Caswell, H. (1987). "A stage-based population model for loggerhead sea turtles and implications for conservation". Ecology. 68 (5): 1412–1423. doi:10.2307/1939225. JSTOR 1939225.CS1 maint: Multiple names: authors list (link)
  • Ellner, S.P., Fieberg, J., Ludwig, D., and Wilcox, C. (2002). "Precision of population viability analysis". Conservation Biology. 16: 258–261. doi:10.1046/j.1523-1739.2002.00553.x.CS1 maint: Multiple names: authors list (link)
  • Gilpin, M.E. and Soulé, M.E. (1986). “Conservation biology: The Science of Scarcity and Diversity”, Sunderland, Massachusetts: Sinauer Associates
  • Hui, C., Fox, G.A., and Gurevitch, J. (2017). "Scale-dependent portfolio effects explain growth inflation and volatility reduction in landscape demography". Proceedings of the National Academy of Sciences USA. 114 (47): 12507–12511. doi:10.1073/pnas.1704213114. PMC 5703273.CS1 maint: Multiple names: authors list (link)
  • Perrins, C.M., Lebreton, J.D., and Hirons, G.J.M. (eds.) (1991). “Bird population studies: relevance to conservation and management”, New York: Oxford University Press
  • McCarthy, M.A., Keith, D., Tietjen, J., Burgman, M.A., Maunder M.N., Master, L., Brook, B.W., Mace, G., Possingham, H.P., Medellin, R., Andelman, S., Regan, H., Regan, T., and Ruckelshaus, M (2004). "Comparing predictions of extinction risk using models and subjective judgment". Acta Oecologica. 26 (2): 67–74. doi:10.1016/j.actao.2004.01.008.CS1 maint: Multiple names: authors list (link)
  • Maunder M.N. (2004). "Population Viability Analysis, Based on Combining Integrated, Bayesian, and Hierarchical Analyses". Acta Oecologica. 26 (2): 85–94. doi:10.1016/j.actao.2003.11.008.
  • Menges, E.S. (2000). "Population viability analyses in plants: challenges and opportunities". Trends in Ecology & Evolution. 15 (2): 51–56. doi:10.1016/S0169-5347(99)01763-2. PMID 10652555.
  • Morris, W.F. , Hudgens, B.R., Moyle, L.C., Stinchcombe, J.R., and Bloch, P.L. (2002). "Population viability analysis in endangered species recovery plans: Past use and future improvements". Ecological Applications. 12 (3): 708–712. doi:10.1890/1051-0761(2002)012[0708:PVAIES]2.0.CO;2. ISSN 1051-0761.CS1 maint: Multiple names: authors list (link)
  • Reed, J.M., L.S. Mills, J.B. Dunning, E.S. Menges, K.S. Mckelvey, R. Frye, S.R. Beissinger, M. Anstett, and P. Miller. (2002). "Emerging issues in population viability analysis". Conservation Biology. 16: 7–19. doi:10.1046/j.1523-1739.2002.99419.x.CS1 maint: Multiple names: authors list (link)
  • Taylor, B.L. (1995). "The reliability of using population viability analysis for risk classification of species" (PDF). Conservation Biology. 9 (3): 551–559. doi:10.1046/j.1523-1739.1995.09030551.x.

External links

  • GreenBoxes code sharing network. Greenboxes (Beta) is a repository for open-source population modeling and PVA code. Greenboxes allows users an easy way to share their code and to search for others shared code.
  • VORTEX. VORTEX is an individual-based simulation software that incorporates deterministic forces as well as demographic, environmental and genetic stochastic events on wildlife populations.
  • RAMAS. Widely-accepted software packages for PVA with options for age/stage structure, spatial processes, and landscape change. Models can be built and run using a graphic user interface or users can incorporate the program's batch mode into automated workflows.

Bacterivores are free-living, generally heterotrophic organisms, exclusively microscopic, which obtain energy and nutrients primarily or entirely from the consumption of bacteria. Many species of amoeba are bacterivores, as well as other types of protozoans. Commonly, all species of bacteria will be prey, but spores of some species, such as Clostridium perfringens, will never be prey, because of their cellular attributes.


A copiotroph is an organism found in environments rich in nutrients, particularly carbon. They are the opposite to oligotrophs, which survive in much lower carbon concentrations.

Copiotrophic organisms tend to grow in high organic substrate conditions. For example, copiotrophic organisms grow in Sewage lagoons. They grow in organic substrate conditions up to 100x higher than oligotrophs.


Decomposers are organisms that break down dead or decaying organisms, and in doing so, they carry out the natural process of decomposition. Like herbivores and predators, decomposers are heterotrophic, meaning that they use organic substrates to get their energy, carbon and nutrients for growth and development. While the terms decomposer and detritivore are often interchangeably used, detritivores must ingest and digest dead matter via internal processes while decomposers can directly absorb nutrients through chemical and biological processes hence breaking down matter without ingesting it. Thus, invertebrates such as earthworms, woodlice, and sea cucumbers are technically detritivores, not decomposers, since they must ingest nutrients and are unable to absorb them externally.

Dominance (ecology)

Ecological dominance is the degree to which a taxon is more numerous than its competitors in an ecological community, or makes up more of the biomass.

Most ecological communities are defined by their dominant species.

In many examples of wet woodland in western Europe, the dominant tree is alder (Alnus glutinosa).

In temperate bogs, the dominant vegetation is usually species of Sphagnum moss.

Tidal swamps in the tropics are usually dominated by species of mangrove (Rhizophoraceae)

Some sea floor communities are dominated by brittle stars.

Exposed rocky shorelines are dominated by sessile organisms such as barnacles and limpets.

Energy Systems Language

The Energy Systems Language, also referred to as Energese, Energy Circuit Language, or Generic Systems Symbols, was developed by the ecologist Howard T. Odum and colleagues in the 1950s during studies of the tropical forests funded by the United States Atomic Energy Commission. They are used to compose energy flow diagrams in the field of systems ecology.

Feeding frenzy

In ecology, a feeding frenzy occurs when predators are overwhelmed by the amount of prey available. For example, a large school of fish can cause nearby sharks, such as the lemon shark, to enter into a feeding frenzy. This can cause the sharks to go wild, biting anything that moves, including each other or anything else within biting range. Another functional explanation for feeding frenzy is competition amongst predators. This term is most often used when referring to sharks or piranhas. It has also been used as a term within journalism.


A lithoautotroph or chemolithoautotroph is a microbe which derives energy from reduced compounds of mineral origin. Lithoautotrophs are a type of lithotrophs with autotrophic metabolic pathways. Lithoautotrophs are exclusively microbes; macrofauna do not possess the capability to use mineral sources of energy. Most lithoautotrophs belong to the domain Bacteria, while some belong to the domain Archaea. For lithoautotrophic bacteria, only inorganic molecules can be used as energy sources. The term "Lithotroph" is from Greek lithos (λίθος) meaning "rock" and trōphos (τροφοσ) meaning "consumer"; literally, it may be read "eaters of rock". Many lithoautotrophs are extremophiles, but this is not universally so.

Lithoautotrophs are extremely specific in using their energy source. Thus, despite the diversity in using inorganic molecules in order to obtain energy that lithoautotrophs exhibit as a group, one particular lithoautotroph would use only one type of inorganic molecule to get its energy.

Local extinction

Local extinction or extirpation is the condition of a species (or other taxon) that ceases to exist in the chosen geographic area of study, though it still exists elsewhere. Local extinctions are contrasted with global extinctions.

Local extinctions may be followed by a replacement of the species taken from other locations; wolf reintroduction is an example of this.

Mark Boyce

Mark Stephen Boyce (born May 24, 1950) is a professor of population ecology in the University of Alberta Department of Biological Sciences, and the Alberta Conservation Association Chair in Fisheries and Wildlife. Among other topics, he has written extensively on population viability analysis and resource selection functions. Early work was on demography and life history evolution. In 1993 he began research on habitat selection and the integration of habitats with population biology. He initiated research on elk in the Greater Yellowstone Ecosystem in 1977 and in 1988 was recruited by the National Park Service to build a simulation model to anticipate the consequences of wolf reintroduction in Yellowstone National Park. These simulation models were published by Yellowstone National Park to justify the ultimate release of wolves in 1995. Several graduate students and postdoctoral fellows continued the Yellowstone work.

After moving to the University of Alberta in 1999 most research has been on mammals and birds in Alberta.In 2014, he was elected as a fellow of the Royal Society of Canada.

Mesotrophic soil

Mesotrophic soils are soils with a moderate inherent fertility. An indicator of soil fertility is its base status, which is expressed as a ratio relating the major nutrient cations (calcium, magnesium, potassium and sodium) found there to the soil's clay percentage. This is commonly expressed in hundredths of a mole of cations per kilogram of clay, i.e. cmol (+) kg−1 clay.


A mycotroph is a plant that gets all or part of its carbon, water, or nutrient supply through symbiotic association with fungi. The term can refer to plants that engage in either of two distinct symbioses with fungi:

Many mycotrophs have a mutualistic association with fungi in any of several forms of mycorrhiza. The majority of plant species are mycotrophic in this sense. Examples include Burmanniaceae.

Some mycotrophs are parasitic upon fungi in an association known as myco-heterotrophy.


An organotroph is an organism that obtains hydrogen or electrons from organic substrates. This term is used in microbiology to classify and describe organisms based on how they obtain electrons for their respiration processes. Some organotrophs such as animals and many bacteria, are also heterotrophs. Organotrophs can be either anaerobic or aerobic.

Antonym: Lithotroph, Adjective: Organotrophic.


Overpopulation occurs when a species' population exceeds the carrying capacity of its ecological niche. It can result from an increase in births (fertility rate), a decline in the mortality rate, an increase in immigration, or an unsustainable biome and depletion of resources. When overpopulation occurs, individuals limit available resources to survive.

The change in number of individuals per unit area in a given locality is an important variable that has a significant impact on the entire ecosystem.


A planktivore is an aquatic organism that feeds on planktonic food, including zooplankton and phytoplankton.

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.

Recruitment (biology)

In biology, especially marine biology, recruitment occurs when a juvenile organism joins a population, whether by birth or immigration, usually at a stage whereby the organisms are settled and able to be detected by an observer.There are two types of recruitment: closed and open.In the study of fisheries, recruitment is "the number of fish surviving to enter the fishery or to some life history stage such as settlement or maturity".

Relative abundance distribution

In the field of ecology, the relative abundance distribution (RAD) or species abundance distribution describes the relationship between the number of species observed in a field study as a function of their observed abundance. The graphs obtained in this manner are typically fitted to a Zipf–Mandelbrot law, the exponent of which serves as an index of biodiversity in the ecosystem under study.

Food webs
Example webs
Ecology: Modelling ecosystems: Other components


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