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Changing measurements or changing movements? Sampling scale and movement model identifiability across generations of biologging technology

机译:改变测量值或改变运动?跨代生物技术的采样规模和运动模型识别

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摘要

Animal movement patterns contribute to our understanding of variation in breeding success and survival of individuals, and the implications for population dynamics. Over time, sensor technology for measuring movement patterns has improved. Although older technologies may be rendered obsolete, the existing data are still valuable, especially if new and old data can be compared to test whether a behavior has changed over time. We used simulated data to assess the ability to quantify and correctly identify patterns of seabird flight lengths under observational regimes used in successive generations of wet/dry logging technology. Care must be taken when comparing data collected at differing timescales, even when using inference procedures that incorporate the observational process, as model selection and parameter estimation may be biased. In practice, comparisons may only be valid when degrading all data to match the lowest resolution in a set. Changes in tracking technology, such as the wet/dry loggers explored here, that lead to aggregation of measurements at different temporal scales make comparisons challenging. We therefore urge ecologists to use synthetic data to assess whether accurate parameter estimation is possible for models comparing disparate data sets before planning experiments and conducting analyses such as responses to environmental changes or the assessment of management actions.
机译:动物的运动方式有助于我们了解个体繁殖成功和存活的变异以及对种群动态的影响。随着时间的流逝,用于测量运动模式的传感器技术得到了改进。尽管可能淘汰了旧技术,但是现有数据仍然很有价值,特别是如果可以比较新数据和旧数据以测试行为是否随时间变化时。我们使用模拟数据评估了在连续几代湿/干测井技术中使用的观测机制下量化和正确识别海鸟飞行长度模式的能力。比较在不同时间范围内收集的数据时,即使使用包含观察过程的推理程序,也必须小心,因为模型选择和参数估计可能会有所偏差。在实践中,仅当降级所有数据以匹配集合中的最低分辨率时,比较才有效。跟踪技术的变化,例如此处探讨的干/湿记录仪,导致了不同时间尺度的测量结果的汇总,使比较变得困难。因此,我们敦促生态学家使用合成数据来评估在计划实验和进行分析(例如对环境变化的响应或评估管理措施)之前比较不同数据集的模型是否可能进行准确的参数估计。

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