Investigating species-environment relationships at multiple scales: Differentiating between intrinsic scale and the modifiable areal unit problem

In ecology, multi-scale analyses are commonly performed to identify the scale at which a species interacts with its environment (intrinsic scale). This is typically carried out using multi-scale species–environment models that compare the relationship between ecological attributes (e.g., species diversity) measured with point data to environmental data (e.g. vegetation cover) for the surrounding area within buffers of multiple sizes. The intrinsic scale is identified as the buffer size at which the highest correlation between environmental and ecological variables occurs. We present the first investigation of how the spatial resolution of remote sensing environmental data can influence the identification of the intrinsic scale using multi-scale species–environment models. Using the virtual ecologist approach we tested this influence using vegetation cover spatial data and a simulated species–environment relationship derived from the same spatial data. By using a simulation model there was a known truth to use as a benchmark to measure accuracy. Our findings indicate that by varying the spatial resolution of the environmental data, the intrinsic scale may be incorrectly identified. In some cases, the errors in the intrinsic scale identified were close to the maximum value possible that could be measured by this experiment. Consequently, multi-scale ecological analyses may not be suitable for distinguishing scale patterns caused by the relationship between an organism and its environment from scale patterns caused by the effect of changing spatial resolution: a phenomenon referred to as the modifiable areal unit problem (MAUP). Thus, observed scale-dependent ecological patterns may be an artefact of the observation of ecological data, not the ecological phenomenon. This study concludes with some suggestions for future work to quantify the effect of the MAUP on multi-scale studies and develop generalisations that can be used to assess when multi-scale analyses have the potential to produce spurious results.