Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly
Authored by Ilkka Hanski, Otso Ovaskainen, Philip J Harrison
Date Published: 2011
DOI: 10.1890/11-0192.1
Sponsors:
Academy of Finland
European Research Council (ERC)
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Abstract
The complexity of mathematical models of ecological dynamics varies
greatly, and it is often difficult to judge what would be the optimal
level of complexity in a particular case. Here we compare the parameter
estimates, model fits, and predictive abilities of two models of
metapopulation dynamics: a detailed individual-based model (IBM) and a
population-based stochastic patch occupancy model (SPOM) derived from
the IBM. The two models were fitted to a 17-year time series of data for
the Glanville fritillary butterfly (Melitaea cinxia) inhabiting a
network of 72 small meadows. The data consisted of biannual counts of
larval groups (IBM) and the annual presence or absence of local
populations (SPOM). The models were fitted using a Bayesian state-space
approach with a hierarchical random effect structure to account for
observational, demographic, and environmental stochasticities. The
detection probability of larval groups (IBM) and the probability of
false zeros of local populations (SPOM) in the observation models were
simultaneously estimated from the time-series data and independent
control data. Prior distributions for dispersal parameters were obtained
from a separate analysis of mark-recapture data. Both models fitted the
data about equally, but the results were more precise for the IBM than
for the SPOM. The two models yielded similar estimates for a random
effect parameter describing habitat quality in each patch, which were
correlated with independent empirical measures of habitat quality. The
modeling results showed that variation in habitat quality influenced
patch occupancy more through the effects on movement behavior at patch
edges than on carrying capacity, whereas the latter influenced the mean
population size in occupied patches. The IBM and the SPOM explained 63\%
and 45\%, respectively, of the observed variation in the fraction of
occupied habitat area among 75 independent patch networks not used in
parameter estimation. We conclude that, while carefully constructed, detailed models can have better predictive ability than simple models, this advantage comes with the cost of greatly increased data
requirements and computational challenges. Our results illustrate how
complex models can be helpful in facilitating the construction of
effective simpler models.
Tags
Uncertainty
environment
movements
ecology
Dispersal
Monte-carlo
Assimilation
Fragmented landscape
Wild animal populations
Melitaea-cinxia