Using multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g. , iSpot , Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such da ta is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evalu ate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world da ta and the measured challenges are defined in collaboration with biologists and environmental s takeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcomes.