Counting and measuring epibenthic organisms from digital photographs: A semiautomated approach

Abstract

Benthic rocky bottom studies often investigate community structure and function where estimates of percentage cover and abundance of epibenthic organisms are required. Nondestructive photographic methods have the advantage of preserving benthic communities for repeated sampling. There is a need to accelerate image processing to make sample analysis more cost efficient and to make the data available in a timely manner. A semiautomated procedure to estimate epibenthic cover and abundance using Adobe Photoshop and the image analysis plug‐in Fovea Pro was developed to meet this need. The method improves upon previous techniques by using color‐based automated selection tools and a species‐coding system. The technique required some manual processing because some species were less suitable for color recognition and the photographs were of inconsistent quality. The semiautomated selection of colony‐forming organisms was validated by comparing it to a strictly manual approach using a data set from Balsfjord/northern Norway. Constrained ordination and Procrustes analyses showed that the automatic and manual methods were equally effective at documenting variation in the species/abundance data along the driving ecological gradient of depth. The minor deviations in species abundance estimation between the two methods (mostly <20%) were unrelated to the depth gradient and thus had negligible influence on the main ecological conclusions of the study. The semiautomated method is up to four times faster than the manual approach, has clear advantages over former benthic image analysis methods, and is well suited for detection of systematic biological patterns like ecological gradients.