Forest inventory data can offer huge numbers of plots that pose challenges not encountered with traditional data. In this first of three units for forest inventories we show that models fitted to large networks having many small plots make noisy predictions, but they can hold as much information as smaller networks of few large plots.

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Exercise. My budget can no longer support the costs of monitoring a full network. I can reduce costs by limiting the number of plots, the sizes of plots, or both. For this study it is important that the network can provide estimates of the effects of predictors on tree abundance. Using the FIA aggregated data as a model, determine the impact of sample size \(n\) versus effort \(E\) (plot area) on estimates of effects from standAge, meanTemp, and annualPrec in xdataCluster for a species of choice.

I might do this:
  1. Fit the model with glm using family = poisson, see code block below.
  2. For small \(n\), refit with a fraction of the plots. I could select them randomly using the sample( n, n*fraction), where n is the number of plots. If you do this, remember that \(x\) and \(y\) must maintain their alignment.
  3. For small \(E\), refit the model with a sample of trees from each plot, as would occur it plot areas were reduced. I can do this with the function rbinom( n, y, fraction ), where y is the count for each plot (see below).

For next time