Abstract
Human activities have altered continental ecosystems worldwide and generated a major environmental crisis, prompting urgent societal questions on how to best produce goods while at the same time securing sustainable ecological services and raising needs to better understand and predict biodiversity and ecosystems dynamics under global changes. To tackle these questions, experimentation on ecosystems is necessary to improve our knowledge of processes and to propose scientifically sound management strategies. Experimental platforms able to manipulate key factors of global change and including state of the art observation methodologies are available worldwide but how to best integrate them has been rarely addressed. Here, we present and discuss the case of the national research infrastructure AnaEE France dedicated to the study of continental ecosystems and designed to congregate complementary experimental approaches in order to facilitate their access and use through a range of distributed and shared services. The conceptual design of AnaEE France includes five modules. Three modules gather experimental facilities along a gradient of experimental control ranging from highly controlled Ecotron facilities, semi-natural field mesocosms to in natura experimental sites covering major continental ecosystems (forests, croplands, grasslands, and lakes). In addition, AnaEE France also includes shared instruments that can be implemented in experiments and analytical platforms specifically dedicated to environmental biology. To promote reuse of data, generalize results and improve predictive models, AnaEE France further gathers modeling and information systems. The implementation of AnaEE France allowed for mutual synergies, improved the technical skills, stimulated new experiments and helped our scientific community to enter into the big data sharing era.
Highlights
Ecosystems provide key ecological services to human societies including provisioning services and the regulation of climate conditions and element cycles (Balmford and Bond, 2005; Cardinale et al, 2012)
Learning from grand challenges in ecological research, we propose guidelines for the construction and operation of such a research infrastructure
Occupancy rate Number of external projects International projects Private sector projects Data & sample use requests Total number of publications Including research-development publications Number of persons trained Number of workshops organized AnaEE France specific cost Costs supported by grants and research organizations Revenues generated by the use of services (e) Manpower (FTE)
Summary
Ecosystems provide key ecological services to human societies including provisioning services (e.g., biomass production) and the regulation of climate conditions and element cycles (Balmford and Bond, 2005; Cardinale et al, 2012). That modeling and analytical progresses lead to better and more accurate understanding and prediction of matter and energy processes through interdisciplinary approaches (e.g., Bashkin, 2002), a major focus in ecological sciences is on the production of quantitative, experimentally testable approaches using advances in our ability to characterize better the influence and cascading effects of heterogeneity at lower levels on higher levels of complexity (from genes to ecosystems, see Loreau, 2010) This challenge strongly urges the need for building novel, collaborative experimental infrastructures since no single effort will be able to provide us the necessary set of tools and data to solve interdisciplinary questions in our research community. Global changes shift the state of the environmental, which leads to changes in the shape and strength of selection on individual and species traits and feedbacks into ecological dynamics (Reiss et al, 2009) To account for these four essential properties, we acknowledge that integrated experimental set-ups should offer a high level of replication over multiple gradients of temporal and spatial scales and strong capacity to unravel complex biological processes. It is urgent to organize experimental set-ups in such a way that they can be complementary and compatible with models and allow more efficient model-data interactions
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