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]
Uses
editPopulation 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]
History
editIn 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.[4] The first PVA is credited to Shaffer.[4]
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.
Examples
editThe 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.[5]
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.[6]
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.[7]
Controversy
editDebates 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).[8] Also, McCarthey et al. (2003)[9] 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).[10]
Future directions
editImprovements 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.[citation needed]
See also
editReferences
edit- ^ Sanderson, Eric (2006). "How Many Animals Do We Want to Save? The Many Ways of Setting Population Target Levels for Conservation". BioScience. 56 (11). Oxford University Press (OUP): 911. doi:10.1641/0006-3568(2006)56[911:hmadww]2.0.co;2. eISSN 1525-3244. ISSN 0006-3568. S2CID 27937209. American Institute of Biological Sciences.
- ^ 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. hdl:1959.4/unsworks_84814. S2CID 91015464.
- ^ Beissenger S.R.; McCullough D.R., eds. (2002). Population Viability Analysis. Chicago: The University of Chicago Press. ISBN 978-0-226-04178-0.
- ^ a b Shaffer, Mark L. (1983). "Determining Minimum Viable Population Sizes for the Grizzly Bear" (PDF). Bears: Their Biology and Management. 5: 133–139. doi:10.2307/3872530. ISSN 1936-0614. JSTOR 3872530.
- ^ 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. S2CID 59046296.
- ^ 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.
- ^ 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. PMC 5513288. PMID 28725349.
- ^ 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 (6776): 512–519. Bibcode:2000Natur.404..385B. doi:10.1038/35006050. PMID 10746724. S2CID 4373715.
- ^ 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. S2CID 59427471.
- ^ Akçakaya H.R.; Sjörgren-Gulve P. (2000). "Population viability analysis in conservation planning: an overview". Ecological Bulletins. 48: 9–21.
Further reading
edit- 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 (1): 262–263. doi:10.1046/j.1523-1739.2002.01426.x. PMID 35701975. S2CID 85769077.
{{cite journal}}: 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. Bibcode:2000Natur.404..385B. doi:10.1038/35006050. PMID 10746724. S2CID 4373715.
{{cite journal}}: 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.
{{cite journal}}: 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 (1): 258–261. doi:10.1046/j.1523-1739.2002.00553.x. PMID 35701961. S2CID 55642940.
{{cite journal}}: 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. Bibcode:2017PNAS..11412507H. doi:10.1073/pnas.1704213114. PMC 5703273. PMID 29109261.
{{cite journal}}: 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. Bibcode:2004AcO....26...67M. doi:10.1016/j.actao.2004.01.008.
{{cite journal}}: 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. S2CID 43987429.
{{cite journal}}: 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 (1): 7–19. doi:10.1046/j.1523-1739.2002.99419.x. PMID 35701959. S2CID 6997697.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - Taylor, B.L. (1995). "The reliability of using population viability analysis for risk classification of species". Conservation Biology. 9 (3): 551–559. doi:10.1046/j.1523-1739.1995.09030551.x.
External links
edit- 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 Archived 2019-11-24 at the Wayback Machine. 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.