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将运动纳入灰海豹种群动态模型。

Incorporating movement into models of grey seal population dynamics.

作者信息

Harrison Philip J, Buckland Stephen T, Thomas Len, Harris Rob, Pomeroy Paddy P, Harwood John

机构信息

Centre for Research into Ecological and Environmental Modelling, The Observatory, University of St Andrews, St Andrews, Fife KY16 9LZ, UK.

出版信息

J Anim Ecol. 2006 May;75(3):634-45. doi: 10.1111/j.1365-2656.2006.01084.x.

DOI:10.1111/j.1365-2656.2006.01084.x
PMID:16689946
Abstract
  1. One of the most difficult problems in developing spatially explicit models of population dynamics is the validation and parameterization of the movement process. We show how movement models derived from capture-recapture analysis can be improved by incorporating them into a spatially explicit metapopulation model that is fitted to a time series of abundance data. 2. We applied multisite capture-recapture analysis techniques to photo-identification data collected from female grey seals at the four main breeding colonies in the North Sea between 1999 and 2001. The best-fitting movement models were then incorporated into state-space metapopulation models that explicitly accounted for demographic and observational stochasticity. 3. These metapopulation models were fitted to a 20-year time series of pup production data for each colony using a Bayesian approach. The best-fitting model, based on the Akaike Information Criterion (AIC), had only a single movement parameter, whose confidence interval was 82% less than that obtained from the capture-recapture study, but there was some support for a model that included an effect of distance between colonies. 4. The state-space modelling provided improved estimates of other demographic parameters. 5. The incorporation of movement, and the way in which it was modelled, affected both local and regional dynamics. These differences were most evident as colonies approached their carrying capacities, suggesting that our ability to discriminate between models should improve as the length of the grey seal time series increases.
摘要
  1. 在开发种群动态的空间明确模型时,最困难的问题之一是运动过程的验证和参数化。我们展示了如何通过将源自标记重捕分析的运动模型纳入一个根据丰度数据时间序列进行拟合的空间明确集合种群模型中,来改进这些运动模型。2. 我们将多站点标记重捕分析技术应用于1999年至2001年期间从北海四个主要繁殖群体的雌性灰海豹收集的照片识别数据。然后将拟合度最佳的运动模型纳入明确考虑了人口统计学和观测随机性的状态空间集合种群模型中。3. 使用贝叶斯方法,将这些集合种群模型拟合到每个群体的20年幼崽产量数据时间序列上。基于赤池信息准则(AIC)的拟合度最佳的模型只有一个运动参数,其置信区间比从标记重捕研究中获得的置信区间小82%,但对于一个包含群体间距离影响的模型也有一些支持。4. 状态空间建模提供了对其他人口统计学参数的改进估计。5. 运动的纳入及其建模方式影响了局部和区域动态。这些差异在群体接近其承载能力时最为明显,这表明随着灰海豹时间序列长度的增加,我们区分模型的能力应该会提高。

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