The impact of interpolated daily temperature data on landscape-wide predictions of invertebrate pest phenology

Insect phenology depends upon temperature, and data from scattered synoptic weather stations are the principle inputs for phenology models used in decision support systems. The paper assesses the spatial dynamics of the penalty, as measured through errors in the timing of predicted insect development stages, that results when entomologists use daily maximum and minimum temperature data from the nearest station to a location, in comparison with an interpolated temperature equivalent, to drive their models. Jack-knife cross-validated estimates of temperature were propagated through an example phenology model, in this case for codling moth (Cydia pomonella). The intention was to contrast the effect of two interpolation methods on phenological results through time at different geographical locations. Use of weather data from the nearest UK meteorological data station (174 points) for phenological modelling doubled the error in predicted development dates for first generation development when compared with the use of landscape-wide interpolated daily temperature data. The results are based on a partial thin plate spline interpolation methodology: the figures are spatial and temporal averages for mainland England and Wales. Overall, spline interpolations provide phenology results that either exceed or are as good as nearest neighbour techniques for 75% of locations over England and Wales, taking first and second generation developmental stages into account. In a minority (21%) of cases nearest neighbour strategies (Voronoi methods) performed better, with an average 18-day improvement in the predictions of development date over the spline method on those occasions. Where splines performed best, their performance exceeded that of the Voronoi method by an average of 25 days. Nearest neighbour techniques did not necessarily perform well in lowland areas, indicating findings of potential significance to those considering input data requirements when modelling insect ecology. (C) 2002 Elsevier Science B.V. All rights reserved.