Fish species identification is critical to the study of fish ecology and management of fisheries. Traditionally, dichotomous keys are used for fish identification. The keys consist of questions about the observed specimen. Answers to these questions lead to more questions till the reader identifies the specimen. However, such keys are incapable of adapting or changing to meet different fish identification approaches, and often do not focus upon distinguishing characteristics favored by many field ecologists and more user-friendly field guides. This makes learning to identify fish difficult for Ichthyology students. Students usually supplement the use of the key with other methods such as making personal notes, drawings, annotated fish images, and more recently, fish information websites, such as Fishbase. Although these approaches provide useful additional content, it is dispersed across heterogeneous sources and can be tedious to access. Also, most of the existing electronic tools have limited support to manage user created content, especially that related to parts of images such as markings on drawings and images and associated notes. We present SuperIDR, a superimposed image description and retrieval tool, developed to address some of these issues. It allows users to associate parts of images with text annotations. Later, they can retrieve images, parts of images, annotations, and image descriptions through text-and content-based image retrieval. We evaluated SuperIDR in an undergraduate Ichthyology class as an aid to fish species identification and found that the use of SuperIDR yielded a higher likelihood of success in species identification than using traditional methods, including the dichotomous key, fish web sites, notes, etc.

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Many scholarly tasks involve working with subdocuments, or contextualized fine-grain information, i.e., with information that is part of some larger unit. A digital library (DL) facilitates management, access, retrieval, and use of collections of data and metadata through services. However, most DLs do not provide infrastructure or services to support working with subdocuments. Superimposed information (SI) refers to new information that is created to reference subdocuments in existing information resources. We combine this idea of SI with traditional DL services, to define and develop a DL with SI (SI-DL). We explored the use of subimages and evaluated the use of SuperIDR, a prototype SI-DL, in fish species identification, a scholarly task that involves working with subimages. The contexts and strategies of working with subimages in SuperIDR suggest new and enhanced support (SI-DL services) for scholarly tasks that involve working with subimages, including new ways of querying and searching for subimages and associated information. The main conceptual contributions of our work are the insights gained from these findings of the use of subimages and of SuperIDR, which lead to recommendations for the design of digital libraries with superimposed information.

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