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Stochastic population dynamics and time to extinction of a declining population of barn swallows
Author(s) -
Engen Steinar,
Sæther Bernterik,
Møller Anders Pape
Publication year - 2001
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1046/j.0021-8790.2001.00543.x
Subject(s) - extinction (optical mineralogy) , population , statistics , population model , population viability analysis , barn , interval (graph theory) , prediction interval , extinction probability , statistic , mathematics , population size , variance (accounting) , econometrics , ecology , biology , geography , demography , economics , endangered species , accounting , combinatorics , paleontology , archaeology , sociology
Summary1 Time to extinction was predicted for a declining population of barn swallow Hirundo rustica in Denmark, using a model that includes demographic as well as environmental stochasticity and that takes the uncertainties in the parameter estimates into account. 2 We apply the concept of population prediction interval (PPI), which is a stochastic interval that includes the unknown variable that may be the extinction time or the population size at some future point of time, with a given probability (1 − α). 3 The lower bound of the upper one‐sided prediction interval for the extinction time for α = 0·10 was 22 years. 4 Ignoring uncertainties in the parameter estimates led to a 41% increase in this statistic. 5 Although the estimate of the demographic variance was small compared to other passerines (σ d 2 = 0·180), a sensitivity analysis showed that it strongly influenced the predicted time to extinction compared to the model ignoring demographic stochasticity. A similar effect on the prediction of the time to extinction was found for the environmental variance σ e 2 . In addition, choosing σ e 2 = 0 strongly reduced the width of the prediction interval. 6 This demonstrates that reliable population projections require modelling of the environmental as well as the demographic stochasticity, and that the uncertainty in the estimates of the model parameters must be taken into account.