Informatics systems to support large-scale wildlife observation data collection

Background/Question/Methods

Collecting wildlife observation data is an activity that many groups and organizations are engaged in. Knowing what animal was present at a location at a specific time allows us to map species distribution, and by following certain rules, give assurance that these data can be aggregated to larger scales and incorporated into larger collections, such as the Global Biodiversity Information Facility (GBIF) or for bird taxa, the Avian Knowledge Network (AKN), and such systems can address different sets of questions relating to scale. The presence and absence of animal species at a location can help influence future studies and inform management decisions. But wildlife observations can come in many different forms with different sets of attributes and metadata, and these associated data often determine how valuable the observation becomes. For example, if the observation includes a photograph, an infallible method of verification can be added for those observations, provided you have wildlife experts, which can boost the confidence in the observation. We describe the long-term and successful implementation of state-scale wildlife observation systems that solve issues associated with systems involving diverse observer and observation types.

Results/Conclusions

We present our work in building observation system across different scales and taxa specifications. Specifically, we show that multiple systems based on common rule sets can be implemented for small sets of observers and taxa, or large sets. We demonstrate implementation of these systems using volunteer-based observer networks for the states of California and Maine and multiple types of observations, with built in verification systems.  We also demonstrate a novel system for managing observations from wildlife camera networks. We suggest that continental or global federated systems of observers and online reporting systems are feasible and desirable to complement existing highly centralized systems.