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How to estimate variability in affinity relationships in partially observed groups of domestic herbivores?

机译:如何估计部分观察到的家畜草食动物群体亲和力关系的变异性?

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摘要

Animal sociability measurements based on inter-individual distances or nearest-neighbour distributions can be obtained automatically with telemetry collars. So far, all the indices that have been used require the whole group to be observed. Here, we propose an index of the variability in affinity relationships in groups of domestic herbivores, whose definition does not depend on group size and that can be used even if some data are missing. This index and its estimators are based on a function that measures how frequently an animal is closer than another one from a third animal. When no data are missing, we show that our estimator and the variance of the sociability matrix sensu Sibbald (considered as the reference method) are strongly correlated. We then consider two cases of missing data. In the first case, some animals are randomly missing, that is, to account for random breakdown of telemetry collars. Our estimator is unbiased by such missing data and its variance decreases as the number of observation dates increases. In the second case, the same animals are missing at all observation dates, that is, in large herds where there are more individuals to be observed than available telemetry collars. Our estimator of affinity variance within a group is biased by such missing data. Thus, it requires changing animals equipped with telemetry collars regularly during the experiment. Conversely, the estimator remains unbiased at the population level, that is, if several independent groups are being analysed. We finally illustrate how this estimator can be used by investigating changes in the variability of affinities according to group size in grazing heifers.
机译:可以使用遥测项圈自动获取基于个体间距离或最近邻分布的动物社交能力。到目前为止,所有已使用的指标都需要对整个组进行观察。在这里,我们提出了一组食草动物亲和力关系的变异性指数,其定义不取决于群的大小,即使缺少一些数据也可以使用。该指数及其估计值基于一项函数,该函数测量一只动物比第三只动物离另一只动物更近的频率。当没有数据丢失时,我们表明我们的估计量与社交矩阵sensu Sibbald(被视为参考方法)的方差高度相关。然后,我们考虑两种情况下的数据丢失。在第一种情况下,某些动物是随机失踪的,也就是说,是由于遥测项圈的随机破坏所致。我们的估计量不受此类缺失数据的影响,并且随着观察日期数量的增加,其方差减小。在第二种情况下,所有观察日期都缺少相同的动物,也就是说,在大群中要观察的个体要多于可用的遥测项圈。我们在一组内的亲和力方差的估计因此类缺失数据而有偏差。因此,需要在实验期间定期更换配备遥测项圈的动物。相反,在人口层次上,也就是说,如果要分析几个独立的群体,估计量仍然没有偏见。最后,我们将根据放牧小母牛的群体规模调查亲和力变异性的变化,说明如何使用此估计量。

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