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The home-range concept: are traditional estimators still relevant with modern telemetry technology?

机译:家庭范围的概念:传统的估算器是否仍与现代遥测技术相关?

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摘要

Recent advances in animal tracking and telemetry technology have allowed the collection of location data at an ever-increasing rate and accuracy, and these advances have been accompanied by the development of new methods of data analysis for portraying space use, home ranges and utilization distributions. New statistical approaches include data-intensive techniques such as kriging and nonlinear generalized regression models for habitat use. In addition, mechanistic home-range models, derived from models of animal movement behaviour, promise to offer new insights into how home ranges emerge as the result of specific patterns of movements by individuals in response to their environment. Traditional methods such as kernel density estimators are likely to remain popular because of their ease of use. Large datasets make it possible to apply these methods over relatively short periods of time such as weeks or months, and these estimates may be analysed using mixed effects models, offering another approach to studying temporal variation in space-use patterns. Although new technologies open new avenues in ecological research, our knowledge of why animals use space in the ways we observe will only advance by researchers using these new technologies and asking new and innovative questions about the empirical patterns they observe.
机译:动物跟踪和遥测技术的最新进展已使收集位置数据的速度和准确性不断提高,这些进展还伴随着新的数据分析方法的发展,这些方法用于描述空间用途,家庭活动范围和利用分布。新的统计方法包括数据密集型技术,例如克里金法和用于栖息地的非线性广义回归模型。此外,源自动物运动行为模型的机械性家庭活动范围模型有望提供新的见解,以了解由于个人对环境的特定运动方式,家庭活动范围如何出现。诸如内核密度估计器之类的传统方法由于易于使用而可能仍然流行。大型数据集使得可以在相对短的时间内(例如几周或几个月)应用这些方法,并且可以使用混合效应模型来分析这些估计值,从而提供了另一种方法来研究空间使用模式的时间变化。尽管新技术为生态研究开辟了新途径,但我们对动物为何以观察方式使用空间的认识只有通过研究人员使用这些新技术并就其观察到的经验模式提出新的和创新的问题,才能有所发展。

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