Abstract: Temperature affects the physiology and population growth of many organisms. Thus, understanding the consequences of changes in its mean and variance is critical in ecology. Nonlinear averaging can be used to study how populations react to variation in environmental factors. In this work, we use nonlinear averaging to examine the expected effect of temperature variation and climate change on population growth rate and population size for various species that are part of the Global Population Dynamics Database (GPDD). From time series of population size and temperature, we estimate the shape of the curve linking temperature and per-capita population growth rate. To understand the uncertainty in these curves, we use bootstrapping techniques. We approximate the effect of temperature on several species’ population growth rates and sizes by using a Taylor approximation based on the local curvatures of the predicted curves. The findings show the variation of nonlinear averaging effect of temperature across different species growth rate. In general, this study fuels the discussion on the predictive power of the value of nonlinear averaging in predicting population growth.
