In order to attach some statement of reliability to mesoscale maps of how pest risk may develop over time, methods were developed to enable the detection and evaluation of errors in predictions that arise from the use of input data series from remote point sources. Firstly, we investigated how predicted model results may differ as a result of the ordering of the spatial interpolation and the model procedures. Principles of logic were used to detect errors occurring in the daily sequences of predicted pest development. Analyses of spatial autocorrelation within the gridded results showed that areas where a pest was predicted to reach a certain stage of development become more fragmented as a model run progressed over time. We identified that the less intensive approach of running a model only at data points and subsequently interpolating these to a grid can, in some cases, result in errors of logic and unrealistic degrees of autocorrelation. These errors occurred particularly when mapping a non-indigenous, marginal, pest at the later stages of its development. As a strategy for error evaluation, deterministic process models were run using point-based estimates of interpolated daily temperature to give RMS data errors at the sample points. This enabled us to investigate how the component of error related to sparsely distributed point data contributed to errors in the gridded estimates of pest development over time. The error detection and evaluation methods outlined are tractable and applicable to a wide variety of cases where point based models running over multiple time steps are extended to provide spatially continuous, landscape-wide, mappable results
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