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Assessing the dynamics of natural populations by fitting individual‐based models with approximate Bayesian computation
Author(s) -
Sirén Jukka,
Lens Luc,
Cousseau Laurence,
Ovaskainen Otso
Publication year - 2018
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12964
Subject(s) - approximate bayesian computation , computer science , population , metapopulation , ibm , bayesian probability , data mining , inference , artificial intelligence , materials science , nanotechnology , biological dispersal , demography , sociology
Individual‐based models ( IBM s) allow realistic and flexible modelling of ecological systems, but their parameterization with empirical data is statistically and computationally challenging. Approximate Bayesian computation ( ABC ) has been proposed as an efficient approach for inference with IBM s, but its applicability to data on natural populations has not been yet fully explored. We construct an IBM for the metapopulation dynamics of a species inhabiting a fragmented patch network, and develop an ABC method for parameterization of the model. We consider several scenarios of data availability from count data to combination of mark‐recapture and genetic data. We analyse both simulated and real data on white‐starred robin ( Pogonocichla stellata ), a passerine bird living in montane forest environment in Kenya, and assess how the amount and type of data affect the estimates of model parameters and indicators of population state. The indicators of the population state could be reliably estimated using the ABC method, but full parameterization was not achieved due to strong posterior correlations between model parameters. While the combination of the data types did not provide more accurate estimates for most of the indicators of population state or model parameters than the most informative data type (ringing data or genetic data) alone, the combined data allowed robust simultaneous estimation of all unknown quantities. Our results show that ABC methods provide a powerful and flexible technique for parameterizing complex IBM s with multiple data sources, and assessing the dynamics of the population in a robust manner.