Abstract
The strategy of scientific model building is the strategy of abstraction. An abstraction is devised to explain the workings of certain mechanistic principles and, as subjective constructs they are neither right nor wrong. Rather, abstractions obscure or enlighten the process under study, yielding useless or useful models (Levins, 2006). Two opposing approaches are usually followed in the process of ecological model construction as a route to understanding (Evans et al., 2013). In the first one, a model is worked out from theoretical principles and a set of testable predictions are then confronted with data from real systems. In the second approach, it is the detailed knowledge of the natural history of a particular system what drives the level of complexity of a mechanistic model used to fit empirical data, and this model is further used to reproduce the dynamics of the original system and, ideally, of other systems. Given the current challenges in the study of population dynamics (Oro, 2013), which abstraction is more useful? The paper by Harrison et al. (2011) is a salient example of the second approach. Using 17-year time series data from a metapopulation of the Glanville fritillary butterfly (Melitaea cinxia Figure 1) inhabiting 72 meadows in Finland, they fit a mechanistic Individual-Based Model (IBM) to spatial biannual counts of larval groups. With the aim of assessing the desirability of complex models with respect to simpler ones in explaining the observed metapopulation dynamics, they also derived a simpler, Stochastic Patch Occupancy Model (SPOM), and fit it to the presence or absence of local populations across time and space. Interestingly, the overall fitting of both models was rather similar, and the bold result is the same with either model: variation in habitat quality influenced patch occupancy mainly through the effects on movement behavior at patch edges. The question immediately arises: does it pay to have expensive, individual-based
Highlights
The strategy of scientific model building is the strategy of abstraction
A model is worked out from theoretical principles and a set of testable predictions are confronted with data from real systems. It is the detailed knowledge of the natural history of a particular system what drives the level of complexity of a mechanistic model used to fit empirical data, and this model is further used to reproduce the dynamics of the original system and, ideally, of other systems
What the modeling strategy of Harrison et al (2011) demonstrates, is that clear-cut definitions of simplicity or generality does not really exist (Evans et al, 2013). Their modeling strategy simultaneously holds several layers of simplicity and complexity: the ecological mechanism of dispersal as a factor connecting a set of spatially distributed populations is relatively simple (Hanski, 1999), the mathematics underpinning models construction is rather complex
Summary
The strategy of scientific model building is the strategy of abstraction. An abstraction is devised to explain the workings of certain mechanistic principles and, as subjective constructs they are neither right nor wrong. A commentary on Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly by Harrison, P.
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