Predicting population fluctuations with artificial neural networks
1998
Lindström, Jan | Kokko, Hanna | Ranta, Esa | Lindén, Harto
Successful predictions of population fluctuations are valuable in game management, as population estimates are instrumental in increasing the time available for management decisions. However, finding a population model which produces predictions accurate enough to be used for management purposes is often precluded due to scarcity and noisiness of population data. Using two long‐term population data sets, 1964–1984 data on Finnish grouse (Tetrao urogallus, T. tetrix and Bonasa bonasia) and 1914–1950 data on coloured fox Vulpes fulva from Canada, we demonstrate the use and power of an artificial neural network in predicting population fluctuations. The performance of an artificial neural network model is compared to two benchmark forecasts: time series mean and the previous data value. Unfortunate as it is, in practise management decisions often have to be made with limited data. Therefore, a notable advantage of neural network modelling is the forecast accuracy even in cases when the time series available are short and noisy, and the processes underlying population fluctuations are not fully understood.
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