restricted access V.2 Population Viability Analysis
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V.2 Population Viability Analysis Daniel F. Doak, Myra E. Finkelstein, and Victoria J. Bakker OUTLINE 1. Overview 2. The history of PVAs 3. Basic components and methods 4. Real-world examples 5. The future of PVAs Population viability analysis (PVA) is the use of quantitative models to predict future population growth and extinction risks. PVA includes a variety of methods to gauge the sensitivity of population viability to natural and human-caused impacts and to estimate the efficacy of management interventions in promoting population growth and safety from extinction. PVA began as a field that borrowed tools from basic population ecology and applied them to conservation questions. From those beginnings, PVA has matured into a discipline that drives innovations in analysis methods and tries more generally to address the processes of conservation planning and priority setting. Because of their wide usage , in particular for assessing management actions, PVA approaches have been closely scrutinized, and the field continues to refine its methods to tackle key criticisms. In summary , PVA has provided specific guidance that has aided the recovery of scores of endangered species and has helped to crystallize several general principles in conservation. GLOSSARY demographic stochasticity. Unpredictability through time in a population’s demography (how many individuals die, how many reproduce, etc.) caused by the randomness of individual fates. This type of stochasticity is usually important only at very small population sizes. environmental stochasticity. Unpredictable changes through time in average demographic rates of a population. These changes can be caused by vacillations in weather, food, predators, or other biotic and abiotic forces influencing individuals in a population and can exert strong effects on the dynamics of populations. genetic stochasticity. Unpredictable changes in gene frequencies as a result of processes such as random genetic drift. This type of stochasticity is usually important only at very small population sizes. inbreeding depression. The decline in measures of individual performance (e.g., survival, growth, or reproduction ) sometimes seen in offspring of parents that are closely related to one another. lambda (k). Annual population growth rate. metapopulation. In general, a collection of populations that are connected by movement. More specifically, the term is usually reserved for a collection of populations each of which has reasonably high probabilities of local extinction and also of recolonization. Nt. Population size in year t. parameters. Values used to describe population dynamics in models, such as the mean or variance in fecundity or survival rate. population viability. The probability of continued existence of a population. Viability is the converse of the risk of extinction (often defined in terms of quasiextinction rather than complete extinction) over some time period. quasiextinction threshold (Nqe). The minimum number of individuals below which a population is likely to be critically and immediately imperiled. 1. OVERVIEW The International Union for Conservation of Nature (IUCN) currently recognizes over 15,000 species as threatened with extinction worldwide (http://www However, given the uncertainty surrounding the status of numerous species or even how many species exist, the number of imperiled species on a global scale is almost certainly considerably more than those documented by the IUCN. The causes of species endangerment vary (see chapter V.1), but in all cases, conservation biologists working to avert extinctions wish to understand the degree of risk facing a particular species or population. Even more importantly , they wish to identify practical management actions that can substantially improve the viability— the long-term chances of persistence—of threatened populations. To answer these questions, the discipline of population viability analysis (PVA) has emerged over the last three decades. PVA, defined broadly, is the use of quantitative methods to predict the likely future status of populations of conservation concern and also to predict how best to manage these populations. PVA grew into a distinct field in ecology because making predictions about population persistence is quite difficult. As Yogi Berra once said (perhaps misquoting a similar observation by Niels Bohr), ‘‘It’s tough to make predictions, especially about the future,’’ and this is particularly true for the population processes described in PVAs, which are typically known only through imperfect data and are influenced by myriad random, or stochastic, forces. PVAs are developed to generate these hard-to-make predictions in a way that is clearly reasoned and quantitative rather than based solely on expert opinion. Importantly, constructing a quantitative PVA model requires explicit articulation of what is known about a population versus what is assumed or...