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影响英国鸟类密度依赖性检测的因素:I. 种群趋势。

Factors influencing detection of density dependence in British birds : I. Population trends.

作者信息

Holyoak Marcel, Baillie Stephen R

机构信息

NERC Centre for Population Biology, Imperial College at Silwood Park, Ascot, UK.

British Trust for Ornithology, Thetford, Norfolk, UK.

出版信息

Oecologia. 1996 Oct;108(1):47-53. doi: 10.1007/BF00333213.

Abstract

We question why density dependence has remained elusive in series of annual abundances of British birds. In particular, an earlier study reported that significant temporal trends in abundances occur in up to 74% of time series from the Common Birds Census. Several studies showed that such trends can hinder detection of density dependence. Temporal trends do not preclude the presence of density dependence and two published tests for density dependence include temporal trends in the null hypothesis model. We explore the extent to which detection of density dependence was hindered by temporal trends in bird abundance data. We used a conservative method to test for trends, which found significant (P<0.05) linear population trends in only 7 of 60 time series of abundances (of 17-31 years) compiled from the Common Birds Census data. However, both of the tests for density dependence that allow for trends and a third method gave P-values that were strongly influenced by the strength of trends, including trends that were not significant (P>0.05). This shows that density dependence may be falsely rejected or detected when trends are present, even when these trends are weak and not statistically significant. To circumvent this problem we detrended the time-series prior to testing for the presence of density dependence. To minimize subjectivity we used simulated time series to check that this procedure did not increase the level of type I error (false rejection of density independence). Additionally, we confirmed that the method gave acceptable levels of type II error, where the test fails to reject density independence in series generated using a density dependent model. This showed that the detrending method was acceptable and represents a major improvement in our ability to detect density dependence in time series that contain temporal trends. Detrending the bird time series increased the number of series in which significant (P<0.05) density dependence was found from 10 (17%), when trends are ignored, to 27 (45%) when series are detrended. However, this rate of 45% is still surprisingly low by comparison to other taxa, and we believe that other factors may contribute to this, which we explore in the second of this pair of papers.

摘要

我们质疑为何在英国鸟类年度数量序列中密度依赖关系仍难以捉摸。特别是,一项早期研究报告称,在来自普通鸟类普查的时间序列中,高达74%的序列存在显著的数量时间趋势。多项研究表明,此类趋势会妨碍对密度依赖关系的检测。时间趋势并不排除密度依赖关系的存在,并且两项已发表的密度依赖关系检验在原假设模型中纳入了时间趋势。我们探究了鸟类数量数据中的时间趋势在多大程度上阻碍了对密度依赖关系的检测。我们使用一种保守方法来检验趋势,该方法在从普通鸟类普查数据汇编的60个数量时间序列(17至31年)中,仅在7个序列中发现了显著(P<0.05)的线性种群趋势。然而,两项考虑趋势的密度依赖关系检验以及第三种方法给出的P值都受到趋势强度的强烈影响,包括那些不显著(P>0.05)的趋势。这表明当存在趋势时,即使这些趋势微弱且无统计学显著性,密度依赖关系也可能被错误地拒绝或检测到。为规避此问题,我们在检验密度依赖关系的存在之前对时间序列进行了去趋势处理。为尽量减少主观性,我们使用模拟时间序列来检查该程序是否未增加I型错误(错误地拒绝密度独立性)的水平。此外,我们确认该方法给出了可接受的II型错误水平,即在使用密度依赖模型生成的序列中,该检验未能拒绝密度独立性。这表明去趋势方法是可接受的,并且代表了我们在检测包含时间趋势的时间序列中的密度依赖关系能力方面的重大改进。对鸟类时间序列进行去趋势处理后,发现显著(P<0.05)密度依赖关系的序列数量从忽略趋势时的10个(17%)增加到去趋势处理后的27个(45%)。然而,与其他分类群相比,45%的比例仍然低得出奇,我们认为其他因素可能对此有影响,我们将在这两篇论文的第二篇中进行探讨。

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