Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data.
Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data.
复制标题
DOI：
10.1371/journal.pone.0040923
复制
发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Lehvävirta S
中科院分区：
文献类型：
--
作者：
Kotze DJ;O'Hara RB;Lehvävirta S
如果处理不当，物种可探测性的时间变化会使相对丰度的估计产生偏差。例如，当努力在空间和/或时间上变化时，在分析数据时就有必要考虑到可探测性的变化。我们证明了将季节性的数据分析的重要性，由于在一个特定的密度的一个物种失去了陷阱的样本量不相等。一个案例研究的计数数据进行了模拟使用弹簧活跃的步甲甲虫。陷阱“丢失”随机在高丰度网站的高甲虫活动，在低丰度网站的低甲虫活动。五种不同的模型被拟合到具有不同损失水平的数据集。如果样本量不等，并且假设个体数量服从对数正态分布的模型中不包括季节性变量，则模型严重低估或高估了真实效应量。结果并没有改善时，季节性和诱捕天数在这些模型中包括作为抵消条款，但只有表现良好时，响应变量被指定为负二项分布。最后，如果一个物种的季节变化是未知的，这是经常发生的情况，季节性可以作为一个自由因子添加，从而产生性能良好的负二项模型。基于这些结果，我们建议（a）增加采样工作量（B）如果研究对象的可探测性的季节性变化的精确信息可用，则将季节性作为偏移项添加到模型中;（c）如果可探测性的季节性变化的信息不充分，则将季节性作为自由因子添加;以及（d）将计数数据的响应变量指定为遵循负二项分布或过度分散泊松分布。
Temporal variation in the detectability of a species can bias estimates of relative abundance if not handled correctly. For example, when effort varies in space and/or time it becomes necessary to take variation in detectability into account when data are analyzed. We demonstrate the importance of incorporating seasonality into the analysis of data with unequal sample sizes due to lost traps at a particular density of a species. A case study of count data was simulated using a spring-active carabid beetle. Traps were ‘lost’ randomly during high beetle activity in high abundance sites and during low beetle activity in low abundance sites. Five different models were fitted to datasets with different levels of loss. If sample sizes were unequal and a seasonality variable was not included in models that assumed the number of individuals was log-normally distributed, the models severely under- or overestimated the true effect size. Results did not improve when seasonality and number of trapping days were included in these models as offset terms, but only performed well when the response variable was specified as following a negative binomial distribution. Finally, if seasonal variation of a species is unknown, which is often the case, seasonality can be added as a free factor, resulting in well-performing negative binomial models. Based on these results we recommend (a) add sampling effort (number of trapping days in our example) to the models as an offset term, (b) if precise information is available on seasonal variation in detectability of a study object, add seasonality to the models as an offset term; (c) if information on seasonal variation in detectability is inadequate, add seasonality as a free factor; and (d) specify the response variable of count data as following a negative binomial or over-dispersed Poisson distribution.
登录
查看更多内容
影响因子：
2.9
作者：
Lehvavirta, Susanna;Kotze, D. Johan;O'Hara, Bob
通讯作者：
O'Hara, Bob
影响因子：
5.2
作者：
Niemelä, J;Kotze, DJ;de Oca, EM
通讯作者：
de Oca, EM
影响因子：
6.6
作者：
O'Hara, Robert B.;Kotze, D. Johan
通讯作者：
Kotze, D. Johan
影响因子：
0.7
作者：
O'Hara, Robert B.
通讯作者：
O'Hara, Robert B.
影响因子：
3.9
作者：
Kotze, DJ;Niemelä, J
通讯作者：
Niemelä, J