Population Viability Analysis
- LAST REVIEWED: 09 June 2020
- LAST MODIFIED: 25 February 2016
- DOI: 10.1093/obo/9780199830060-0142
- LAST REVIEWED: 09 June 2020
- LAST MODIFIED: 25 February 2016
- DOI: 10.1093/obo/9780199830060-0142
Population viability analysis (PVA) (sometimes referred to as extinction risk assessment, population vulnerability analysis, predictive simulation modelling, or stochastic population modelling) describes the process by which data and models are evaluated to determine the risk of population extinction over some given time frame and under specified conditions. Understanding the risks of extinction faced by different populations can help to identify conservation priorities, while the formal consideration of how different interventions affect the probability of persistence can guide the choice of conservation actions. PVA has been a cornerstone of quantitative, species-focused conservation since the 1980s. Although it is sometimes characterized rather narrowly as simply the modelling of population trajectories, PVA is actually a much richer endeavor. For a given population, PVA requires the synthesis of quantitative information regarding the population’s dynamics as well as an explicit consideration of threats to its persistence and options for its management. More generally, the field of PVA provokes questions about realism, generality, and sensitivity in modelling, about the key drivers of population dynamics and extinction, and about realistic time frames for population management. It exposes gaps in existing knowledge, about both general processes and specific populations, and thereby forces consideration of how best to deal with uncertainty. A strong theme in PVA research has been to characterize sources of variability in population dynamics, resulting in important contributions to the understanding of the roles of different types of stochasticity. A great diversity of methods exists for conducting PVA models and a significant challenge is to harmonize these, rendering their outcomes comparable. This has been the focus of recent research into the estimation of minimum viable population size (MVP), one purpose to which PVAs have often been applied. Nonetheless, the estimation and utility of MVP estimates remains controversial. In spite of its long history, PVA remains widely used and integral to several ongoing developments in conservation biology. These include efforts to predict both the impacts of expected climatic change and the consequences of management for interacting species. It is likely that PVA will remain an important analytical process for guiding conservation interventions for many years to come.
Among others, Beissinger and McCullough 2002 and Morris and Doak 2002 (both cited under General Overviews) review the origins of PVAs in some depth. In the 1970s, four factors focused attention on the problems of small populations. These included burgeoning interest in island biogeography and its implications for extinction, especially of populations confined to small areas, which is emphasized in Simberloff 1976; increasing recognition of the importance of variability in population dynamics, which is highlighted in May 1973; a developing science of the relationship between genetics and population size, stressed in Frankel 1974; and a growing awareness of the extinction crisis, which can be seen in Myers 1979 and in the retrospective overview Simberloff 1988. Moving into the 1980s, these concerns dominated the developing field of conservation science, as seen in the treatments in Soulé and Wilcox 1980 and Soulé 1986. Questions associated with the vulnerability of small populations to extinction prompted consideration, in Shaffer 1981, of what constituted a small population and at what size a population ceased to be vulnerable. Ginzburg, et al. 1982 emphasizes the importance of stochastic modelling of quasi-extinction risk in environmental assessment. Shaffer 1983 uses such a stochastic model to estimate the risks of population extinction and, thus, the Minimum Viable Populations for grizzly bears (Ursus arctos). From these beginnings, the concept of PVA emerged (see Soulé 1987, cited under General Overviews). Early proponents, in works such as Burgman, et al. 1988, saw population-focused models of extinction probabilities, informed by high-quality autecological data, as an essential focus for larger questions about the design of reserves and the allocation of conservation resources. Subsequently, however, Caughley 1994 raised concerns about the dominant theoretical focus on small populations. Arguably, conservation biology still struggles to unite the disparate strands of research identified in Caughley 1994 and to ensure that work of academic appeal and theoretical interest contributes meaningfully to arresting rates of extinction. This remains a major challenge in the discipline.
Burgman, M. A., H. R. Akçakaya, and S. S. Loew. 1988. The use of extinction models for species conservation. Biological Conservation 43:9–25.
Summarizes arguments against island biogeography as a predictively useful theory on which to base conservation decisions. The authors argue that population-focused conservation is likely to be more successful than conservation focused on communities or ecosystems. Conservation based on genetic and population dynamic models is promoted.
Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63.2: 215–244.
This seminal paper identifies two parallel approaches to conservation biology: the small population paradigm, providing theoretical insights into the problems faced by small populations; and the declining population paradigm, focused on identifying and mitigating the agents of a population’s decline. Better integration of the two approaches is promoted.
Frankel, O. H. 1974. Genetic conservation: Our evolutionary responsibility. Genetics 78.1: 53–65.
Embodies the growing awareness, in the 1970s, of the need to conserve genetic variability within species, not just the species themselves. Frankel argues that more information is needed on the genetic processes characterizing natural populations, that we must safeguard the evolutionary potential of both wild and domesticated populations, and that genetic considerations can inform conservation practice.
Ginzburg, L. R., L. B. Slobodkin, K. Johnson, and A. G. Bindman. 1982. Quasiextinction probabilities as a measure of impact on population growth. Risk Analysis 2.3: 171–181.
This paper promoted stochastic modelling as a key method in environmental risk assessment. The authors proposed measures for estimating the change in quasi-extinction probabilities as the consequence of an impact and investigated the effects on time to quasi-extinction of aspects of stochasticity.
May, R. M. 1973. Stability in randomly fluctuating versus deterministic environments. American Naturalist 107.957: 621–650.
This paper was key to the increasing focus of conservation biologists on the importance of stochasticity in population dynamics, an important element in the developing science of conservation biology. May shows that stochastic population models can yield outcomes qualitatively different from those of their deterministic analogues.
Myers, N. 1979. The sinking ark: A new look at the problem of disappearing species. Oxford: Pergamon.
Influential treatment of the scale of the biodiversity crisis, focusing on explaining why so many species are doomed to extinction and what drives that fate. Focuses on tropical forests but the lessons are general, especially in regard to consumerism as the ultimate driver of extinction.
Shaffer, M. L. 1981. Minimum population sizes for species conservation. Bioscience 31.2: 131–134.
The development of PVA was inextricably tied to the concept of the Minimum Viable Population (MVP). Posing the question: “how much land is enough to achieve conservation objectives?” Shaffer presents the first tentative definition for the concept of the MVP and discusses methods available to derive MVPs.
Shaffer, M. L. 1983. Determining minimum viable population sizes for the grizzly bear. Bears: Their Biology and Management 5:133–139.
Arguably the first PVA. Presents a stochastic simulation model, with demographic structure, to estimate the minimum population of Yellowstone grizzly bears that would have a 95 percent probability of persisting for one hundred years. Uses those population size estimates to estimate the minimum area requirements of a viable population.
Simberloff, D. 1976. Experimental zoogeography of islands: Effects of island size. Ecology 57:629–648.
Presents empirical data from experimental manipulations of island size among mangrove islands in the Florida Keys. Data supported the principles of island biogeography, emphasizing that extinction rates will be higher in smaller areas.
Simberloff, D. 1988. The contribution of population and community biology to conservation science. Annual Review of Ecology and Systematics 19.1: 473–511.
Discusses the background to the developing science of conservation biology, which also prompted developments in PVA. Simberloff identifies the importance of population ecology to that science; however, he also notes the complexities it introduces (for example, where it indicates that populations have very low probabilities of persistence).
Soulé, M. E. 1986. Conservation biology: The science of scarcity and diversity. Sunderland, MA: Sinauer.
This edited volume, including contributions from forty-five authors, helped to define the modern discipline of conservation biology. It includes important contributions on elements of the small population paradigm in Caughley 1994 and seeks to identify how those can contribute to conservation in the real world. Gilpin and Soulé’s chapter introduced the term “population viability analysis.”
Soulé, M. E., and B. A. Wilcox. 1980. Conservation biology: An ecological-evolutionary perspective. Sunderland, MA: Sinauer.
Perhaps the first key text in developing the small population focus of conservation biology in the 1980s, this edited volume covers a range of topics and introduces some key definitions. It was this book that introduced Franklin’s often-quoted 50/500 rule (see Minimum Viable Populations).
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