Introduction

Attempts to understand how biodiversity originates and is maintained, and how it contributes to ecosystem functioning and human services, are hindered by lack of complete information. To understand the complexity of ecosystem function, and the likely impacts of human activities on these functions, ecologists and conservation scientists need to understand species interactions across multiple scales. Most studies to date have attempted to gain this understanding by looking at a very small subset of species, focusing primarily on vertebrates and other well-known or 'charismatic' groups. Unfortunately, recent syntheses (e.g. Goldwasser and Roughgarden, 1997; Platnick, 1999) suggest that such studies are not adequate in terms of predicting biodiversity patterns or signatures of disturbance. Additional information on lesser known groups is required to complete the picture. Yet, most studies avoid collecting data on diverse groups such as insects and arachnids precisely because they are less well known!

The inclusion of these groups in biodiversity studies has traditionally required both trained personnel who are able to identify known species correctly, and a systematist who can recognize and describe specimens new to science. Even when knowledgeable personnel can be found, the process of identification and description of new species takes time and money - assets in short supply for most ecologists, conservation biologists and wildlife managers. Non-specialists do not have the training or access to the materials necessary to produce accurate and consistent identifications on their own. The combined effect of this has heretofore led to the use and interpretation of questionable data or, more commonly, the complete abandonment of data from those taxonomic groups that comprise the bulk of biodiversity. We cannot hope to understand the complexity of ecosystem function and the relationship of human activities with ecosystem function without knowing how many, and what kinds of organisms are present.

Faced with these problems, and the increasing demand internationally for biodiversity research, some partial solutions have been pursued that attempt to delay or circumvent altogether the need for identifications. The use of technicians, or parataxonomists, to collect, sort and catalogue specimens prior to the input of a specialist has met with some success in Costa Rica (Instituto Nacional de Biodiversidad [INBio], 2001). The designation of RTUs (recognizable taxonomic units), or morphospecies, by non-specialists in order to obtain rapid richness estimates without requiring species-level identifications has proved reasonably accurate and useful in some cases (Oliver and Beattie, 1993, 1996). Certainly, the creation of biodiversity data-bases that catalogue collected specimens - particularly those that incorporate digital images of whole specimens and search procedures (similar to interactive keys) to help with identification (e.g. VirBas in Australia; Oliver et al., 2000) - will facilitate rapid, albeit cursory, biodiversity assessments. Although these methods provide a way to obtain quick species counts for initial richness comparisons, they do not provide enough information for in-depth biological or ecological studies. For serious analyses, identity is important. Therefore, tools must be developed to make routine identifications of specimens by non-experts both accurate and efficient.

An ideal identification system is one that encapsulates the knowledge of a systematist, requires little user input, and yields quick and accurate identifications. Some computer-aided identification systems such as interactive keys, multi-access keys, hypertext keys and expert systems are a significant improvement over the traditional, printed dichotomous key, but still require significant input from the user (and therefore require basic knowledge of the morphology and terminology of the target group; see Edwards and Morse, 1995; Dodd and Rosendahl, 1996; Rambold and Agerer, 1997). Methods that exhibit some level of automation are likely to be more accessible to non-specialists.

Many partly automated identification systems for multicellular organisms make use of digital imaging (e.g. Gerhards et al., 1993; Dietrich and Pooley, 1994; Chtioui et al., 1996; Weeks et al., 1997; Kwon and Cho, 1998; Do et al., 1999; Mancuso and Nicese, 1999; Weeks et al., 1999; Theodoropoulos et al., 2000). In very general terms, information is extracted from images in the form of specific measurements (taken manually or with the help of image tool programs), or the image itself is processed into a form that can be expressed numerically. The extracted observations are then subjected to statistical analysis (e.g. PCA, discriminant analysis), or submitted to some form of artificial neural network (ANN) in order to characterize and subsequently classify the species. Artificial neural networks are programming algorithms that simulate the structure of the brain and its processing of information (see Boddy et al., 1990, for an introduction). Species identification using ANNs, although similar in principle to statistical classification, relies on the ANN itself to create the group 'classifiers' by selectively weighting the input characters and adjusting its own internal configuration to maximize identification accuracy.

In the development of our identification system, we chose to focus on the ANN approach. This decision was based on a number of factors, including previous studies showing that in situations where both statistical and ANN-based approaches were tried using the same data as inputs, the ANNs almost always achieved equivalent or superior levels of accuracy (Chtioui et al., 1996; Goodacre et al., 1996; Wilkins et al., 1996; Parsons and Jones, 2000). The advantage of using ANNs is greatest when traditional identification procedures rely on somewhat subjective, qualitative characters that cannot be simply quantified (or even necessarily described). Qualitative features are subject to inter-and intra-observer variability arising from the user's level of knowledge, experience and frequency of use (Theodoropoulos et al., 2000).

There have already been many promising studies evaluating the potential of neural networks for the identification of cell types and organisms. ANNs have been used successfully in medical research to identify and classify cancer cells (Maollemi, 1991; Jiang et al., 1996; Hurst et al., 1997); to identify microorganisms of various kinds, including bacteria, yeasts and phytoplankton (Rataj and Schindler, 1991; Kennedy and Thakur, 1993; Goodacre et al., 1996; Wilkins et al., 1996; Goodacre et al., 1998; Wit and Busscher, 1998); and to identify macro-organisms, including plants of agricultural interest (Chtioui et al., 1996; Kwon and Cho, 1998; Mancuso and Nicese, 1999), parasitic larvae (Theodoropoulos et al., 2000), spiders (Do et al., 1999) and bats (from their echolocation signals - Parsons and Jones, 2000).

Of course, there are many different kinds of neural networks, ways of structuring an identification system and approaches to making such a system available to the public and there are many challenges to be faced when working with real data. Our system, SPIDA (species identification, automated), or the web-accessible version, SPIDA-web, was created as a generalized identification system that can be tailored for virtually any group of organisms that can be distinguished visually (i.e. prior testing had demonstrated early versions' ability to distinguish five species of Ichneumonid wasp [unpublished data], six species of Lycosid spiders [Do et al., 1999] and twelve species of North American bees [Russell et al., in prep]). That said, by choosing to develop and refine our system using real data with which we have succeeded in creating a working prototype, we have of necessity had to face a number of challenges that will be common to most if not all automated identification systems.

Our test case, the Australasian ground spiders of the family Trochanteriidae, provided good examples of these challenges, including, among others, intraspecific variability (which itself varies in degree across species), variability in sample quality (due to debris or imaging techniques) and small sample sizes. In addition, we decided to tackle the problem of identifying all the closely related species included in a major taxon instead of the much simpler problem of distinguishing the species that happen to co-occur in a single area, most of which are only distantly related to each other and hence relatively easy to separate. Finally, spiders are considered by some to be one of the more difficult groups in terms of assigning species-level identifications, even compared with other arthropods. In the USA, only a tiny fraction of the roughly 3500 species are identifiable without the use of a microscope and the appropriate technical keys. Traditionally, one needs first to determine family membership with one key, genus membership with a different key (focusing on entirely different structures) and then, finally, species membership focusing on the complex structures of the genitalia, described in dizzying technical detail in published monographs. In sum, we have given ourselves a difficult task. But by doing so, we can more realistically assess the

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