Introduction

At present, the most severely limiting factor on our understanding of community structure, diversity, and how diversity relates to ecosystem function and services, is the lack of experts capable of identifying biological specimens to species. For most groups of organisms, the number of trained systematists is low, and the success rate of non-specialists trying to achieve accurate identifications on their own, with currently available tools, is even lower. The situation is worst for relatively small and inconspicuous organisms (i.e., precisely those groups that comprise the bulk of our planet’s biodiversity). One way to ameliorate this problem is to encapsulate the taxonomic expertise of a specialist into a computerized identification system. A system that can identify any species in a particular family, or from a particular area, without requiring the user to have more than the most basic knowledge of the organism to be identified, has the potential to drastically improve the efficiency and scope of biological inventories, and subsequent monitoring efforts.

We are currently developing an Internet-accessible automated identification system that uses artificial neural networks to make identifications based on digital images. We call this system SPIDA-web (SPecies IDentification, Automated and web accessible). Our test group for the prototype is one of the world’s most diverse—spiders, Order Araneae. We are developing the system from two perspectives: taxonomic (Family Trochanteriidae) and geographic (surveys conducted in Knox Co., TN). The feasibility of larger systems (or, more realistically, hierarchically linked arrangements of smaller systems) remains to be tested.  But a successful prototype could pave the way for widespread use of the technology in studying diverse taxa, and lead to a subsequent explosion of knowledge about the species composition of our biosphere.

Objectives