Natural resource management and biodiversity conservation increasingly require cost-effective proxies of biodiversity and species abundance that are applicable across a range of spatial scales. We outline a rapid, efficient and low-cost measure of the spectral signal of digital habitat images that (i) requires little image processing or interpretation, (ii) provides an effective proxy for habitat complexity and (iii) correlates with species diversity. We validated this method for coral reefs of the Great Barrier Reef (GBR, Australia) across a range of spatial scales (1 m – 10 km), using digital photographs of benthic communities at the transect scale, and high-resolution Landsat satellite images at the reef scale. We calculated the mean information gain (MIG), i.e., an index of image-derived spatial heterogeneity, at each scale. We then related MIG to univariate (species richness and total abundance summed across species) and multivariate (species abundance matrix) measures of fish community structure, using two techniques that account for the hierarchical structure of the data: hierarchical (mixed-effect) linear models and distance-based partial redundancy analysis.
Results/Conclusions
Over the length and breadth of the GBR, MIG alone explained up to 29% of deviance in fish species richness, 33% in total fish abundance and 25% in fish community structure at multiple scales, thus demonstrating the possibility of easily and rapidly exploiting spatial information contained in digital images to complement existing methods for inferring diversity and abundance patterns among fish communities. Therefore, after minimal processing, the spectral signal of remotely sensed images provides an efficient and low-cost way to optimize the design of surveys used in conservation planning. In data-sparse situations, this simple approach also offers a viable method for rapid assessment of potential local biodiversity, particularly where there is little local capacity for mounting in-depth biodiversity surveys.