The Tropical Ecology, Assessment and Monitoring (TEAM) Network: An early warning system for tropical rain forests.

While there are well established early warning systems for a number of natural phenomena (e.g. earthquakes, catastrophic fires, tsunamis), we do not have an early warning system for biodiversity. Yet, we are losing species at an unprecedented rate, and this especially occurs in tropical rainforests, the biologically richest but most eroded biome on earth. Unfortunately, there is a chronic gap in standardized and pan-tropical data in tropical forests, affecting our capacity to monitor changes and anticipate future scenarios. The Tropical Ecology, Assessment and Monitoring (TEAM) Network was established to contribute addressing this issue, as it generates real time data to monitor long-term trends in tropical biodiversity and guide conservation practice. We present the Network and focus primarily on the Terrestrial Vertebrates protocol, that uses systematic camera trapping to detect forest mammals and birds, and secondarily on the Zone of Interaction protocol, that measures changes in the anthroposphere around the core monitoring area. With over 3 million images so far recorded, and managed using advanced information technology, TEAM has created the most important data set on tropical forest mammals globally. We provide examples of site-specific and global analyses that, combined with data on anthropogenic disturbance collected in the larger ecosystem where monitoring sites are, allowed us to understand the drivers of changes of target species and communities in space and time. We discuss the potential of this system as a candidate model towards setting up an early warning system that can effectively anticipate changes in coupled human-natural system, trigger management actions, and hence decrease the gap between research and management responses. In turn, TEAM produces robust biodiversity indicators that meet the requirements set by global policies such as the Aichi Biodiversity Targets. Standardization in data collection and public sharing of data in near real time are essential features of such system.