Scholarlink

<p>Biologging devices have revolutionised our understanding of aquatic animal movement by enabling the collection of detailed depth and temperature time-series. The advent of pop-up satellite archival tags has been particularly impactful, facilitating the collection of tens of thousands of depth time-series (DTS) datasets, with deployment periods ranging from days to years. Datasets from recovered tags are more detailed than those transmitted via satellite, yet both are commonly reported with rudimentary histograms of time-at-temperature and time-at-depth. Such histograms often fail to capture the complex temporal dynamics of vertical movements that are available from the high sampling frequency time-series in recovered tags. This study describes a robust and effective methodological workflow for the quantitative analysis of large DTS datasets collected from archival tags deployed on gill-breathing aquatic animals, utilising continuous wavelet transformation (CWT), Principal Component Analysis (PCA), and k-means clustering. CWT was employed to detect key periodic patterns within the data. Daily wavelet components were calculated across different wavelet periods (e.g., 5-min through 24-h) and reduced via PCA to characterise daily vertical movement behaviour while preserving variance. Finally, unsupervised k-means clustering was used to classify vertical movement behaviours according to their wavelet components and depth summary statistics. This approach efficiently processed large quantities of data, and validation using simulated data demonstrated its robustness and versatility, with assigned behaviour clusters matching the original simulated behaviour types with high consistency (97.7%). For the empirical data, distinct behavioural clusters were identified across a wide range of species, including an oceanic manta ray <i>Mobula birostris</i>, whale shark <i>Rhincodon typus</i>, Atlantic cod <i>Gadus morhua</i>, and largetooth sawfish <i>Pristis pristis</i>. Down sampling of the DTS revealed the workflow to be somewhat insensitive to the sampling frequency of tags, maintaining 83.9% consistency as sampling frequency decreased from one to 15-minutes. These results not only underscore the workflow’s efficacy but also highlight its broad applicability in diverse settings. To facilitate uptake of this approach, an R package <i>FishDiveR</i>, tailored for the implementation of this analytical methodological workflow has been developed.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

FishDiveR: wavelet analyses and machine learning provide robust classification of animal behaviour from time-depth data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月22日

查看全文
  • Calvin S. Beale,
  • Jenna L. Hounslow,
  • Angela J. E. Beer,
  • Matias Braccini,
  • Mark V. Erdmann,
  • Alastair Harry,
  • Neil R. Loneragan,
  • Mark Meekan,
  • Stephen J. Newman,
  • David Righton,
  • Ferawati Runtuboy,
  • Michael J. Travers,
  • Serena Wright,
  • Adrian C. Gleiss

摘要

Biologging devices have revolutionised our understanding of aquatic animal movement by enabling the collection of detailed depth and temperature time-series. The advent of pop-up satellite archival tags has been particularly impactful, facilitating the collection of tens of thousands of depth time-series (DTS) datasets, with deployment periods ranging from days to years. Datasets from recovered tags are more detailed than those transmitted via satellite, yet both are commonly reported with rudimentary histograms of time-at-temperature and time-at-depth. Such histograms often fail to capture the complex temporal dynamics of vertical movements that are available from the high sampling frequency time-series in recovered tags. This study describes a robust and effective methodological workflow for the quantitative analysis of large DTS datasets collected from archival tags deployed on gill-breathing aquatic animals, utilising continuous wavelet transformation (CWT), Principal Component Analysis (PCA), and k-means clustering. CWT was employed to detect key periodic patterns within the data. Daily wavelet components were calculated across different wavelet periods (e.g., 5-min through 24-h) and reduced via PCA to characterise daily vertical movement behaviour while preserving variance. Finally, unsupervised k-means clustering was used to classify vertical movement behaviours according to their wavelet components and depth summary statistics. This approach efficiently processed large quantities of data, and validation using simulated data demonstrated its robustness and versatility, with assigned behaviour clusters matching the original simulated behaviour types with high consistency (97.7%). For the empirical data, distinct behavioural clusters were identified across a wide range of species, including an oceanic manta ray Mobula birostris, whale shark Rhincodon typus, Atlantic cod Gadus morhua, and largetooth sawfish Pristis pristis. Down sampling of the DTS revealed the workflow to be somewhat insensitive to the sampling frequency of tags, maintaining 83.9% consistency as sampling frequency decreased from one to 15-minutes. These results not only underscore the workflow’s efficacy but also highlight its broad applicability in diverse settings. To facilitate uptake of this approach, an R package FishDiveR, tailored for the implementation of this analytical methodological workflow has been developed.