Tools for detecting misidentified species in biodiversity databases

[mjwestgate]

Biodiversity databases such as the Global Biodiversity Information Facility (GBIF) or Atlas of Living Australia (ALA) contain large amounts of open data, but also face persistent challenges in detecting 'wrong' points; observations of plants and animals that appear to be in the wrong place, be allocated to the wrong species, or both. Detecting these records is notoriously difficult for those who lack expert knowledge of species biogeography.

Currently, the error-detection tools available to these institutions are conceptually very simple, relying on detecting points that are outside of expert-provided polygons, or that are outliers in climate space relative to other members of their species. In contrast, there are no tools that use:

• More advanced statistical methods, such as latent variable models or machine learning;
• Information on data providers that may be indicative of shared 'types' of error (spatial, temporal or taxonomic); or
• Data on species relatedness to suggest alternative taxa that might resemble those in mislabelled observations

We propose bringing some example datasets to workshop alternative statistical methods that may improve detection of errors, relative to traditional methods.

[deanmarchiori]

Hi! Thanks for the topic suggestion.

To help us prepare for the hackathon event it would be great to prepare a quick 30-60 sec overview of the topic to introduce it to the group on the day and seek interested collaborators. You can use the below prompts to help with this:

What is the headline idea?

What is the (realistic) outcome being aimed for during the event?

What types of contributions would be welcomed (i.e. specific skills, tasks)?

[mjwestgate]

Link to repo Link to frog data Link to species distribution maps