@article {JRN54598,
	title = {A toolkit for ecosystem ecologists in the time of Big Science},
	journal = {Ecosystems},
	year = {2016},
	abstract = {<p class="rtejustify">Ecosystem ecologists are being challenged to address the increasingly complex problems that comprise Big Science. These problems include multiple levels of biological organization that cross multiple interacting temporal and spatial scales, from individual plants, animals, and microbes to landscapes, continents, and the globe. As technology improves, the availability of data, derived data products, and information to address these complex problems are increasing at finer and coarser scales of resolution, and legacy, dark data are brought to light. Data analytics are improving as big data increase in importance in other fields that are improving access to these data. New data sources (crowdsourcing, social media) and ease of communication and collaboration among ecosystem ecologists and other disciplines are increasingly possible via the internet. It is increasingly important that ecosystem ecologists be able to communicate their findings, and to translate their concepts and findings into concrete bits of information that a general public can understand. Traditional approaches that portray ecosystem sciences as a dichotomy between empirical research and theoretical research will keep the field from fully contributing to the complexity of global change questions, and will keep ecosystem ecologists from taking full advantage of the data and technology available. Building on previous research, we describe a more forward-looking, integrated empirical{\textendash} theoretical modeling approach that is iterative with learning to take advantage of the elements of Big Science. We suggest that training ecosystem ecologists in this integrated approach will be critical to addressing complex Earth system science questions, now and in the future.</p>
},
	keywords = {analytics, big data, crowdsourcing, interacting spatial and temporal scales, machine learning, multiple levels of organization, technological advances},
	doi = {10.1007/s10021-016-0072-1},
	url = {files/bibliography/JRN00645.pdf},
	author = {Debra C. Peters and Gregory S. Okin}
}
