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<p>Understanding movement patterns is crucial for predicting species responses to environmental change. However, in aquatic environments, the lack of long-term movement data constrains our ability to interpret how species adapt to changing conditions. Fish otoliths provide insights into individual life-history strategies via time-resolved chemical signatures but disentangling interactions between physiological and environmental processes remain challenging. Current analytical approaches often categorise life-history profiles into qualitative groups, based on sampling regions or years, overlooking intra-annual variation and simplifying data to group-level means. This study introduces a non-linear statistical framework using Generalised Additive Mixed Models (GAMMs) to explore how otolith element concentrations (Mg:Ca, Sr:Ca, Ba:Ca) vary with demographic variables (Age, Region) and temporal effects in a tropical snapper and illustrates its application on <i>Lutjanus malabaricus</i> across the Indo-Pacific region. Our findings revealed significant, non-linear changes in otolith Sr:Ca, reflecting shared and region-specific age patterns, suggesting that despite geographic separation, phylogenetic processes affecting Sr regulation remained consistent between regions. In contrast, distinct region-specific age differences in Ba:Ca and Mg:Ca highlight the influence of environmental and/or physiological processes on otolith chemistry. Incorporating random effects refined our analysis by accounting for temporal dependencies and individual-specific age patterns. Overall, this non-linear framework provides a powerful approach to unravelling the complex life-history and movement strategies in fish populations, providing critical insights into their adaptive responses to changing environments. Beyond otoliths, this framework can be applied to analyse continuous, time-resolved chemical data from accretionary structures across aquatic taxa, capitalising on the diverse ecological information archived across the natural world.</p>

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A non-linear statistical framework to investigate changes in life history patterns within and among fish populations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月20日

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  • Clement Z. W. Ng,
  • Patrick Reis-Santos,
  • Júlio G. Gonzalez,
  • Bronwyn M. Gillanders,
  • Muhammad F. Saleh,
  • Joyce J. L. Ong

摘要

Understanding movement patterns is crucial for predicting species responses to environmental change. However, in aquatic environments, the lack of long-term movement data constrains our ability to interpret how species adapt to changing conditions. Fish otoliths provide insights into individual life-history strategies via time-resolved chemical signatures but disentangling interactions between physiological and environmental processes remain challenging. Current analytical approaches often categorise life-history profiles into qualitative groups, based on sampling regions or years, overlooking intra-annual variation and simplifying data to group-level means. This study introduces a non-linear statistical framework using Generalised Additive Mixed Models (GAMMs) to explore how otolith element concentrations (Mg:Ca, Sr:Ca, Ba:Ca) vary with demographic variables (Age, Region) and temporal effects in a tropical snapper and illustrates its application on Lutjanus malabaricus across the Indo-Pacific region. Our findings revealed significant, non-linear changes in otolith Sr:Ca, reflecting shared and region-specific age patterns, suggesting that despite geographic separation, phylogenetic processes affecting Sr regulation remained consistent between regions. In contrast, distinct region-specific age differences in Ba:Ca and Mg:Ca highlight the influence of environmental and/or physiological processes on otolith chemistry. Incorporating random effects refined our analysis by accounting for temporal dependencies and individual-specific age patterns. Overall, this non-linear framework provides a powerful approach to unravelling the complex life-history and movement strategies in fish populations, providing critical insights into their adaptive responses to changing environments. Beyond otoliths, this framework can be applied to analyse continuous, time-resolved chemical data from accretionary structures across aquatic taxa, capitalising on the diverse ecological information archived across the natural world.