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Stand together against extinction.
Codex blends structured wildlife research with artificial intelligence, citizen science, and computer vision to speed population analysis and develop new insights to help fight extinction.
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The Problem
According to a July 2017 study in the Proceedings of the National Academy of Sciences, a “sixth mass extinction” is underway, a trend signalled by widespread vertebrate losses that “will have negative cascading consequences on ecosystem functioning and services vital to sustaining civilization.” This meta-study is based on multiple, independent analyses and represents a growing awareness in the wildlife research community that more rapid assessment, response, and review are needed to understand and counter this decline.
Unfortunately, wildlife research efforts are frequently underfunded and small scale. The collection and management of wildlife data remains a largely ad hoc and academic exercise focused on moving small data sets (often in Excel and Access) into local, custom population studies for “one-off” analyses without long-term data curation or collaboration, particularly across borders and regions. Arriving at a critical mass of data for population analysis can take years, especially for rare or endangered species. Long required observation periods and manual data processing (e.g., matching photos “by eye”) can create multi-year lags between study initialization and scientific results, as well as create conclusions too coarse or slow for effective and optimizable conservation action. This limits the scope, scale, repeatability, continuity, and ROI of the studies as they face the limits of their home-grown tools and IT capabilities.
Wildlife researchers lack a common yet customizable platform for collaboration and often don’t have the technical expertise or budget to take advantage of advanced computing tools, and the manual management of data prevents researchers from leveraging the potential of citizen scientists contributing through tourism or volunteerism.
Unfortunately, wildlife research efforts are frequently underfunded and small scale. The collection and management of wildlife data remains a largely ad hoc and academic exercise focused on moving small data sets (often in Excel and Access) into local, custom population studies for “one-off” analyses without long-term data curation or collaboration, particularly across borders and regions. Arriving at a critical mass of data for population analysis can take years, especially for rare or endangered species. Long required observation periods and manual data processing (e.g., matching photos “by eye”) can create multi-year lags between study initialization and scientific results, as well as create conclusions too coarse or slow for effective and optimizable conservation action. This limits the scope, scale, repeatability, continuity, and ROI of the studies as they face the limits of their home-grown tools and IT capabilities.
Wildlife researchers lack a common yet customizable platform for collaboration and often don’t have the technical expertise or budget to take advantage of advanced computing tools, and the manual management of data prevents researchers from leveraging the potential of citizen scientists contributing through tourism or volunteerism.
Our Solutions
Our software blends structured wildlife research with artificial intelligence, citizen science, and computer vision to speed population analysis and develop new insights to help fight extinction. As an open source software framework, our tools supports collaborative mark-recapture, molecular ecology, and social ecology studies, especially where citizen science and artificial intelligence can help scale up projects. Whether working with a legacy Wildbook or with the newly released Codex, the tool provides a technical foundation (database, APIs, computer vision, etc.) for wildlife research projects to:
The biological and statistical communities already support a number of excellent tools, such as Program MARK,GenAlEx, and SOCPROG for use in analyzing wildlife data. Wildbook is a complementary software application that provides:
- track individual animals in a wildlife population using natural markings , genetic identifiers, or vocalizations
- collect biological samples from a wildlife population and performing genetic and/or chemical analyses (e.g., stable isotope measurements, haplotype determination, etc.)
- engage citizen scientists and\or using social media to collect sighting information
- build a collaborative, distributed research network for a migratory and/or global species
- develop a new animal biometrics solution (e.g., pattern matching from photos) for one or more species
- collect behavioral and/or social data for a wildlife study population
The biological and statistical communities already support a number of excellent tools, such as Program MARK,GenAlEx, and SOCPROG for use in analyzing wildlife data. Wildbook is a complementary software application that provides:
- scalable and collaborative platform for intelligent wildlife data storage and management, including advanced, consolidated searching
- easy-to-use software suite of functionality that can be extended to meet the needs of wildlife projects, especially where individual identification is used
- APIs to support the easy export of data to cross-disciplinary analysis applications (e.g., GenePop ) and other software (e.g., Google Earth)
- easy data access to animal biometrics and facilitates matching application deployment for multiple species exposure of data in biodiversity databases (e.g., GBIF and OBIS) through a shared platform
Powered by Machine Learning
Our tools allows for easier storage of data and brings together researchers and citizen scientists to create bigger data sets, the issue of curation and data management becomes a more pressing one. Codex and Wildbook leverage computer vision machine learning to process images, locate animals, apply species labels, and even suggest matching individuals from within the database. This computer vision pipeline works with real-world conditions, allowing for broad contributions. This process gives greater confidence that we know who the animals are and where the have been. Getting this baseline information correct allows researchers to focus on deeper analysis that can bring about actionable change.