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

Abstract

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.

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