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Time series analysis for physiological and endocrinological data: a practical guide.

Joshua Reed1, Cory J D Matthews2, Jennifer Jelincic3

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Summary
This summary is machine-generated.

Wildlife endocrinology can be better understood using time series analysis. This method helps researchers analyze hormone fluctuations over time, revealing how stressors impact wild and captive animals.

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Area of Science:

  • Wildlife endocrinology
  • Behavioral ecology
  • Physiological ecology

Background:

  • Endocrinology studies typically use single samples, limiting understanding of dynamic hormonal changes.
  • Hormone secretion and environmental stressors fluctuate over time within individuals.
  • Temporal data collection offers deeper insights into wildlife physiology and behavior.

Purpose of the Study:

  • To review classic time series analysis methods for wildlife endocrinology.
  • To demonstrate the application of time series analysis with practical examples and R code.
  • To enhance the understanding of temporal and individual variation in hormone levels.

Main Methods:

  • Review of established time series analysis techniques.
  • Application of methods to endocrinology data with worked examples.
  • Provision of R code for practical implementation.

Main Results:

  • Time series analysis addresses challenges in interpreting temporally correlated endocrinology data.
  • The approach reveals natural hormone fluctuations and stressor influences over time.
  • Integration of these methods improves the analysis of wildlife endocrine data.

Conclusions:

  • Time series analysis is a valuable tool for wildlife endocrinology research.
  • This approach enhances the understanding of individual and temporal hormone variations.
  • Wider adoption of these methods can advance the field of wildlife endocrinology.