Effect of uneven sampling along an environmental gradient on transfer-function performance

We investigate the effect that uneven sampling of the environmental gradient has on transfer-function performance using simulated community data. We find that cross-validated estimates of the root mean squared error of prediction can be strongly biased if the observations are very unevenly distributed along the environmental gradient. This biased occurs because species optima are more precisely known (and more analogues are available) in the part of the gradient with most observations, hence estimates are most precise here, and compensate for the less precise estimates in the less well sampled parts of the gradient. We find that weighted averaging and the modern analogue technique are more sensitive to this problem than maximum likelihood, and suggest a way to remove the bias via a segment-wise RMSEP procedure.