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Quantifying the magnitude of a population decline with Bayesian time-series modelling

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Quantifying the magnitude of a population decline with Bayesian time-series modelling Population abundances tend to vary year to year. This variation can makeit make it hard detect a change and hard to quantify exactly what thatchange is. Bayesian time-series analysis can help us quantify a decline and putuncertainty bounds on it too. Here I will use the R-INLApackage to fit a time-series model to apopulation decline. For instance, take the pictured time-series. Quantifying change as thedifference between the first and last time-points is obviouslymisleading. Doing so would imply that abundance has declined by 77% fromthe historical value. Another approach would be to compare the average of the first and lastdecades. Doing so would yield a 72% decline. A better way might be to model the population trend over time and thenestimate our change from the model. An advantage of…
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