Cost-effective proxies of biodiversity and species abundance, applicable across
a range of spatial scales, are needed for setting conservation priorities and planning action. We
outline a rapid, efficient, and low-cost measure of spectral signal from digital habitat images
that, being an effective proxy for habitat complexity, correlates with species diversity and
requires little image processing or interpretation. We validated this method for coral reefs of
the Great Barrier Reef (GBR), Australia, across a range of spatial scales (1 m to 10 km), using
digital photographs of benthic communities at the transect scale and high-resolution Landsat
satellite images at the reef scale. We calculated an index of image-derived spatial
heterogeneity, the mean information gain (MIG), for each scale and related it 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. 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.
Thus, the spectral signal of unprocessed remotely sensed images provides an efficient and lowcost
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 in terms of skills or resources
for mounting in-depth biodiversity surveys.