Supp Sarah R, Bohrer Gil, Fieberg John, La Sorte Frank A
Data Analytics Program, Denison University, Granville, OH, 43023, USA.
Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, 43210, USA.
Mov Ecol. 2021 Dec 11;9(1):60. doi: 10.1186/s40462-021-00294-2.
As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.
随着人类和自动传感器网络收集到越来越大量的动物观测数据，利用这些数据推断或追踪物种移动的新机会随之出现。大规模出现数据集的来源包括众包数据库，如eBird和iNaturalist、气象监测雷达以及被动自动传感器，包括声学监测单元和相机陷阱网络。此类数据资源代表给定位置通常在物种层面的静态观测。尽管如此，通过结合多个地点和时间的多次观测，有可能推断出空间上连续的种群层面移动。种群层面移动描述了构成一个种群的个体的聚集移动，例如范围收缩、扩张、气候追踪或迁徙，这些可能是由物理、行为或人口过程导致的。希望从这种形式的出现数据中对种群移动进行建模，催生了一个不断发展的领域，该领域创造了新的分析和统计方法，能够考虑观测中的空间和时间采样偏差。种群层面移动研究发展所产生的见解可以补充重点追踪研究的见解，并阐明在可能更大的空间和时间尺度上驱动种群分布变化的机制。本综述将总结当前的大规模出现数据集，讨论在种群层面移动分析中利用这些数据集的最新方法，并突出此类分析提供了生态见解的研究。我们概述了从目前存在的陆地动物空间分布出现数据推断移动的概念方法和常见方法步骤，尽管类似方法可能适用于植物、淡水或海洋生物。
