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Automated measurement of upper thermal limits in small aquatic animals.

Tim BurtonBettina ZeisSigurd Einum
Published in: The Journal of experimental biology (2018)
We present a method for automating the measurement of upper thermal limits in small aquatic organisms. Upper thermal limits are frequently defined by the cessation of movement at high temperature, with measurement being performed by manual observation. Consequently, estimates of upper thermal limits may be subject to error and bias, both within and among observers. Our method utilises video-based tracking software to record the movement of individuals when exposed to high, lethal temperatures. We develop an algorithm in the R computing language that can objectively identify the loss of locomotory function from tracking data. Using independent experimental data, we validate our approach by demonstrating the expected response in upper thermal limits to acclimation temperature.
Keyphrases
  • machine learning
  • risk assessment
  • high temperature
  • deep learning
  • electronic health record
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  • physical activity
  • artificial intelligence