Population growth in snow geese: A modeling approach integrating demographic and survey information

Gauthier, Gilles and Besbeas, Panagiotis and Lebreton, Jean-Dominique and Morgan, Byron J. T. (2007) Population growth in snow geese: A modeling approach integrating demographic and survey information. Ecology, 88 (6). 1420-1429 . ISSN 0012-9658 . (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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There are few analytic tools available to formally integrate information coming from population surveys and demographic studies. The Kalman filter is a procedure that facilitates such integration. Based on a state-space model, we can obtain a likelihood function for the survey data using a Kalman filter, which we may then combine with a likelihood for the demographic data. In this paper, we used this combined approach to analyze the population dynamics of a hunted species, the Greater Snow Goose ( Chen caerulescens atlantica), and to examine the extent to which it can improve previous demographic population models. The state equation of the state-space model was a matrix population model with fecundity and regression parameters relating adult survival and harvest rate estimated in a previous capture-recapture study. The observation equation combined the output from this model with estimates from an annual spring photographic survey of the population. The maximum likelihood estimates of the regression parameters from the combined analysis differed little from the values of the original capture-recapture analysis, though their precision improved. The model output was found to be insensitive to a wide range of coefficient of variation ( CV) in fecundity parameters. We found a close match between the surveyed and smoothed population size estimates generated by the Kalman filter over an 18-year period, and the estimated CV of the survey (0.078-0.150) was quite compatible with its assumed value (similar to 0.10). When we used the updated parameter values to predict future population size, the model underestimated the surveyed population size by 18% over a three-year period. However, this could be explained by a concurrent change in the survey method. We conclude that the Kalman filter is a promising approach to forecast population change because it incorporates survey information in a formal way compared with ad hoc approaches that either neglect this information or require some parameter or model tuning

Item Type: Article
Uncontrolled keywords: Chen caerulescens atlantica; Greater Snow Goose; hunting mortality; integrated population modeling; Kalman filter; matrix population model; population dynamics
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Louise Dorman
Date Deposited: 25 Apr 2008 13:20
Last Modified: 09 May 2014 10:29
Resource URI: https://kar.kent.ac.uk/id/eprint/2855 (The current URI for this page, for reference purposes)
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