Landscape complexity and spatial scale influence the relationship between remotely sensed spectral diversity and survey-based plant species richness

Questions: Species rarefaction curves have long been used for estimating the expected number of species as a function of sampling effort. Nonetheless, sampling species based on standard plant inventories represents an effort-intensive approach. Hence, rarefaction based on remotely sensed information can provide a rapid tool for identifying regions with exceptional richness and turnover. The aim of this paper is to examine (i) if the rates of spectral and species accumulation are positively correlated with one another at different spatial scales, and (ii) if the strength of this correlation differs between regions of varying landscape complexity. Location: Switzerland, Europe. Methods: The plant species data were derived from the Swiss “Biodiversity Monitoring” programme. Seven Landsat ETM+ images covering the whole study area were acquired. We applied species and spectral rarefaction for five biogeographical areas ranging from flat to mountainous zones. The relative increments (rates) of the species and spectral rarefaction curves were compared using Pearson correlation together with locally weighted scatterplot smoothing (LOWESS). Results: The biogeographic regions differed from one another in both their spectral and species diversity. The relationship between spectrally- and species-derived rates of accumulation was non-significant in simple landscapes, but we observed a significant positive correlation in complex landscapes over fine-to-intermediate spatial scales. Conclusions: Spectral rarefaction represents a powerful tool for measuring landscape diversity and potentially predicting species diversity at regional to global spatial scales. Based on remotely sensed information, more efficient diversity-based monitoring programmes can be developed.