The problem of scale in predicting biological responses to climate

Many analyses of biological responses to climate rely on gridded climate data derived from weather stations, which differ from the conditions experienced by organisms in at least two respects. First, the microclimate recorded by a weather station is often quite different to that near the ground surface, where many organisms live. Second, the temporal and spatial resolutions of gridded climate datasets derived from weather stations are often too coarse to capture the conditions experienced by organisms. Temporally and spatially coarse data have clear benefits in terms of reduced model size and complexity, but here we argue that coarse‐grained data introduce errors that, in biological studies, are too often ignored. However, in contrast to common perception, these errors are not necessarily caused directly by a spatial mismatch between the size of organisms and the scale at which climate data are collected. Rather, errors and biases are primarily due to (a) systematic discrepancies between the climate used in analysis and that experienced by organisms under study; and (b) the non‐linearity of most biological responses in combination with differences in climate variance between locations and time periods for which models are fitted and those for which projections are made. We discuss when exactly problems of scale can be expected to arise and highlight the potential to circumvent these by spatially and temporally down‐scaling climate. We also suggest ways in which adjustments to deal with issues of scale could be made without the need to run high‐resolution models over wide extents. Many analyses of biological responses to climate rely on coarse spatial and temporal resolution climate data. The use of such data are likely to cause errors, but these errors are not caused directly by spatial mismatch between the size of organisms and the scale of climate data. Rather, errors arise because of the non‐linearity of most biological responses in combination with differences in climate variance between locations and time periods for which models are fitted and those for which predictions are made.