2.1.5.2 Model-based Pros & Cons
A model-based spatial design relies on selection of sites based on the need to estimate parameters or coefficients of a model that will be used to make the population estimates. Such models typically include one or more independent variables or covariates suhc as environmental conditions or habitat quality. Sites are generally selected along the important gradients governing the model parameters. A simple model might be a relationship between a population’s growth rate and temperature. Sites might be selected at locations covering a thermal gradient over the range of the population’s thermal tolerance. Then the model would be used to estimate productivity across all sites in the domain. A restricted model-based spatial design refers to situations in which the selection of locations in part of the domain is guided by the candidate model, and locations in other parts are selected by other methods.
Tools:
None
Pros and Cons:
The following pros and cons of model-based spatial monitoring designs should assist you in determining if it is appropriate for your monitoring needs.
Site Selection
Pros: (if model is “correct”):
- Can provide efficient selection of sites to estimate model parameters.
- Can be cost-effective when a model requires estimates for a limited number of model parameters
Cons:
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Difficult to specify models and assess their potential biases
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Poor models may yield inefficient selection of sites and revisit patterns
- Financial costs: need more up-front time
Statistical Inference (summary/interpretation of results)
Pros: (if model is “correct”):
- Can provide unbiased estimates of model parameters and subsequently for the population
Cons:
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Can only use model-based inference (true only for geostatistical model
