Ecology is poised to enter a new era of ecological forecasting powered by advances in data science, model-data assimilation, and quantitative ecological theory. Recent papers and books demonstrate the power and appeal of this approach for improving understanding and management of fisheries, water resources, and forest carbon cycling, for example. However, the real barrier to progress here is not in any computation or theoretical roadblock, but rather accepting that science and ecology are fundamentally human endeavors, undertaken by people, with varying capacities. I will discuss the human factor in ecoinformatics. How do we as scientists work in this arena? How do enhance the capacity of human networks and collaborations? How do we advance student training?
Results/Conclusions
These are not easy questions to answer but addressing them is the sine qua non for ecological forecasting to actual be something more than a fad. Primarily, I will discuss successes and failures at tackling the human factor in a few collaborations and projects such as the Ameriflux network, NEON, LTER, and the Predictive Ecosystem Analyzer (PEcAn).