Four Facts Every Conservation Biologists Should Know about Persistence

Mangel, Marc; Tier, Charles

doi:10.2307/1941719

Cited by 204 publications

(129 citation statements)

References 26 publications

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“…Interestingly, these predictions from these deterministic models coincide with the predictions of stochastic models used for population viability analysis (Goodman, 1987;Mangel and Tier, 1994). For stochastic models, extinction is inevitable due to demographic stochasticity, and the distribution of extinction times are often approximately exponentially distributed (Goodman, 1987;Mangel and Tier, 1994). Thus (Mangel and Tier, 1994, p. 611) '' in an 'ensemble world view' the mean time to extinction'' in deterministic models exhibiting essential extinction and stochastic models ''is achieved by the average of lots of very rapid extinctions with some very long persistences.''…”

Section: Chaotic Transients and Population Viabilitysupporting

confidence: 61%

Allee effects, extinctions, and chaotic transients in simple population models

Schreiber

2003

Theoretical Population Biology

221

148

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Section: Chaotic Transients and Population Viabilitysupporting

confidence: 61%

Allee effects, extinctions, and chaotic transients in simple population models

Schreiber

2003

Theoretical Population Biology

221

148

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“…However, their sample cannot be assumed to represent all cases and, in principle, it seems probable that small and endangered populations are more likely to show changed dynamics over time as a result of either environmental, anthropogenic or intrinsic processes than are large and unendangered populations. Catastrophic events rapidly decrease the size of a population and could have precipitated the majority of extinctions 14 . The frequency distribution and consequence of such events is rarely known 15 ; consequently, parameterizing a population model to include the probability of a catastrophe occurring in a specified time period is little more than guesswork.…”

Section: Criterion Two: Future Distributionsmentioning

confidence: 99%

The use and abuse of population viability analysis

Coulson

Mace

Hudson

et al. 2001

Trends in Ecology & Evolution

424

292

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A recent study by Brook et al. empirically tested the performance of population viability analysis (PVA) using data from 21 populations across a wide range of species. The study concluded that PVAs are good at predicting the future dynamics of populations. We suggest that this conclusion is a result of a bias in the studies that Brook et al. included in their analyses. We present arguments that PVAs can only be accurate at predicting extinction probabilities if data are extensive and reliable, and if the distribution of vital rates between individuals and years can be assumed stationary in the future, or if any changes can be accurately predicted. In particular, we note that although catastrophes are likely to have precipitated many extinctions, estimates of the probability of catastrophes are unreliable.Population viability analysis (PVA) is a modelling tool that estimates the future size and risk of extinction for populations of organisms 1,2 . PVA works by using life-history or population growth-rate data to parameterize a population model that is then used to project dynamics and estimate future population size and structure 3 . User-friendly PVA software packages allow conservation managers to predict future population sizes and risks of extinction for any population they choose 3 . Because of this ease of application of PVAs, it is important to determine and understand the limits to their predictive accuracy 1,4-6 . Brook et al. have tested the predictive accuracy of PVA using data from many populations and conclude that PVA is not a useless tool, and that it should not be dispensed with in favour of alternative untested methods. Do PVAs work?The predictive accuracy of a PVA will depend on the purpose to which it is being applied. In practice, there has been a range of alternative uses. PVAs can be used to: (1) predict the future size of a population 1,5,6 ; (2) estimate the probability of a population going extinct over a given time 5 ; (3) assess which of a suite of management or conservation strategies is likely to maximize the probability of a population persisting 7 ; and (4) explore the consequences of different assumptions on population dynamics for small populations 8 . In reality, only the predictive accuracy of the first two cases is estimable, as there are rarely sufficient replicate populations from which to collect data to determine whether the comparative predictions of the third use are accurate, and the fourth use has not generated testable predictions.There are two ways that the predictive accuracy of PVAs can be assessed. The first approach is to use historical data and, at a point in the future, predict the population size and compare this to what actually happened. To avoid circularity, the data used to parameterize the model should not include data from the time-period over which predictions are made. The whole population exists as a suite of spatially structured local populations) can then be compared to the distribution of predicted population sizes from the model projections...

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“…The analytic study Of stochastic extinction models is an exciting field that has great educational value (Goodman, 1987;Lande, 1993), but applied problems usually include complexities beyond the assumptions of these models. One of the greatest practical difficulties with analytic and numerical models can be estimating higher level parameters from data, although steps have been made to expedite this process (Talent, 1990;Mangel & Tier, 1994).…”

Section: Discussionmentioning

confidence: 99%

Alex: a model for the viability analysis of spatially structured populations

Possingham¹

1995

Biological Conservation

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A new generic model for assessing the viability of spatially structured populations, ALEX (Analysis of the Likelihood of EXtinction), is described. Strengths and weaknesses of ALEX are discussed. ALEX only models one sex, ignores genetics, and is inadequate for modelling the dynamics of very small populations. However ALEX contains four features that make it useful for assessing the merits of different management options for populations that are distributed in a spatially complex landscape: (1) ALEX allows each patch to have different qualities including a habitat variable that may respond to catastrophes. In this way the dynamics of species which prefer a particular successional stage of a habitat can be modelled. (2) ALEX allows the user to specify a wide variety of catastrophic processes that affect and may depend on population size and/or the state of the habitat in a patch. (3) Sensitivity analysis is essential to the PVA process. ALEX allows automatic sensitivity analysis of most parameters. Although demographic stochasticity is modelled, ALEX can quickly simulate the dynamics of very large populations. (4) Modelling movement between patches by individuals is an important part of the dynamics of spatially structured populations. ALEX permits two types of movement by individuals. This allows the user to explore the importance of corridors, habitat selection, and mortality associated with dispersal.

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Four Facts Every Conservation Biologists Should Know about Persistence

Cited by 204 publications

References 26 publications

Allee effects, extinctions, and chaotic transients in simple population models

Allee effects, extinctions, and chaotic transients in simple population models

The use and abuse of population viability analysis

Alex: a model for the viability analysis of spatially structured populations

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