Biodiversity informatics is the application of informatics techniques to biodiversity information, such as taxonomy, biogeography or ecology. It is defined as the application of Information technology technologies to management, algorithmic exploration, analysis and interpretation of primary data regarding life, particularly at the species level organization. Modern computer techniques can yield new ways to view and analyze existing information, as well as predict future situations (see niche modelling). Biodiversity informatics is a term that was only coined around 1992 but with rapidly increasing data sets has become useful in numerous studies and applications, such as the construction of taxonomic databases or geographic information systems. Biodiversity informatics contrasts with "bioinformatics", which is often used synonymously with the computerized handling of data in the specialized area of molecular biology.

Biodiversity informatics (different but linked to bioinformatics) is the application of information technology methods to the problems of organizing, accessing, visualizing and analyzing primary biodiversity data. Primary biodiversity data is composed of names, observations and records of specimens, and genetic and morphological data associated to a specimen. Biodiversity informatics may also have to cope with managing information from unnamed taxa such as that produced by environmental sampling and sequencing of mixed-field samples. The term biodiversity informatics is also used to cover the computational problems specific to the names of biological entities, such as the development of algorithms to cope with variant representations of identifiers such as species names and authorities, and the multiple classification schemes within which these entities may reside according to the preferences of different workers in the field, as well as the syntax and semantics by which the content in taxonomic databases can be made machine queryable and interoperable for biodiversity informatics purposes...

Biodiversity Informatics can be considered to have commenced with the construction of the first computerized taxonomic databases in the early 1970s, and progressed through subsequent developing of distributed search tools towards the late 1990s including the Species Analyst from Kansas University, the North American Biodiversity Information Network NABIN, CONABIO in Mexico, INBio in Costa Rica, and others, the establishment of the Global Biodiversity Information Facility in 2001, and the parallel development of a variety of niche modelling and other tools to operate on digitized biodiversity data from the mid-1980s onwards (e.g. see ). In September 2000, the U.S. journal Science devoted a special issue to "Bioinformatics for Biodiversity", the journal Biodiversity Informatics commenced publication in 2004, and several international conferences through the 2000s have brought together biodiversity informatics practitioners, including the London e-Biosphere conference in June 2009. A supplement to the journal BMC Bioinformatics (Volume 10 Suppl 14) published in November 2009 also deals with biodiversity informatics.

According to correspondence reproduced by Walter Berendsohn, the term "Biodiversity Informatics" was coined by John Whiting in 1992 to cover the activities of an entity known as the Canadian Biodiversity Informatics Consortium, a group involved with fusing basic biodiversity information with environmental economics and geospatial information in the form of GPS and GIS. Subsequently, it appears to have lost any obligate connection with the GPS/GIS world and be associated with the computerized management of any aspects of biodiversity information (e.g. see )

One major goal for biodiversity informatics is the creation of a complete master list of currently recognised species of the world. This goal has been achieved to a large extent by the Catalogue of Life project which lists >2 million species in its 2022 Annual Checklist. A similar effort for fossil taxa, the Paleobiology Database documents some 100,000+ names for fossil species, out of an unknown total number.

Application of the Linnaean system of binomial nomenclature for species, and uninomials for genera and higher ranks, has led to many advantages but also problems with homonyms (the same name being used for multiple taxa, either inadvertently or legitimately across multiple kingdoms), synonyms (multiple names for the same taxon), as well as variant representations of the same name due to orthographic differences, minor spelling errors, variation in the manner of citation of author names and dates, and more. In addition, names can change through time on account of changing taxonomic opinions (for example, the correct generic placement of a species, or the elevation of a subspecies to species rank or vice versa), and also the circumscription of a taxon can change according to different authors' taxonomic concepts. One proposed solution to this problem is the usage of Life Science Identifiers (LSIDs) for machine-machine communication purposes, although there are both proponents and opponents of this approach.

Organisms can be classified in a multitude of ways (see main page Biological classification), which can create design problems for Biodiversity Informatics systems aimed at incorporating either a single or multiple classification to suit the needs of users, or to guide them towards a single "preferred" system. Whether a single consensus classification system can ever be achieved is probably an open question, however the Catalogue of Life has commissioned activity in this area which has been succeeded by a published system proposed in 2015 by M. Ruggiero and co-workers.

Biodiversity maps provide a cartographic representation of spatial biodiversity data. This data can be used in conjunction with Species Checklists to help with biodiversity conservation efforts. Biodiversity maps can help reveal patterns of species distribution and range changes. This may reflect biodiversity loss, habitat degradation, or changes in species composition. Combined with urban development data, maps can inform land management by modeling scenarios which might impact biodiversity.