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.

Bridging experiments with models is a key issue for research infrastructure. Models can contribute to the experimental process for protocol design or data quality control. Moreover, they offer an efficient way for promoting data reuse thus giving a strong added value to data bases. Therefore, building interoperability between models and experimental platform data bases is an important task to improve the quality of experimental infrastructure and provide users with seamless and integrated information systems. The research infrastructure AnaEE- France is taken as an example illustrating the required steps to achieve such an objective. In AnaEE-France, models are gathered in four thematic modelling platforms (RECORD, VSOIL, CAPSIS and the Centre for Biodiversity Theory and Modelling). They offer services as a repository for modules and models, tools for simulation pre-processing and post- processing and coupling technology to develop new models by taking profit of existing modules. The coupling capacities will be extended in AnaEE-France to the experimental data bases. As the infrastructure is distributed among 21 experimental services, data base frameworks are proposed i) to facilitate the integration of core measurements provided by the experimental platforms and measurements made by users, ii) to standardize data annotation with metadata, and iii) to manage data access rights. To control the semantic, a common referential for both modelling platforms and data bases is under development based on the existing thesauri and the specific AnaEE-France vocabularies. It will provide a thesaurus and simple ontologies to describe the data (traits or parameter, sites, units, spatial and temporal characteristics, methods). Web-services are being developed to access the data bases from the modelling platforms. In a first step, the web-services will be parameterized case-by-case. However, it is foreseen to develop in a second step automatic filters that will take profit of data annotation to match model inputs and outputs with experimental data.

AnaEE France (Analysis and Experimentation on Ecosystems - France) is a national research infrastructure for the study of continental ecosystems (aquatic and terrestrial) and their biodiversity. It offers the scientific community a broad range of 21 services in the form of experimental platforms (in controlled, semicontrolled or natural environments), analysis platforms and shared instruments. AnaEE France also provides access to data and modeling platforms. Some of the platforms are particularly well suited to behavioural ecology studies

Emotions are now largely recognised as a core element in animal welfare issues. However, convenient indicators to reliably infer emotions are still needed. As such, the availability of behavioural postures analogous to facial expressions in humans would be extremely valuable for animal studies of emotions. The purpose of this paper is to find out stable expressive postures in sheep and to relate these expressive postures with specific emotional contexts. In an initial experiment, we identified discrete ear postures from a comprehensive approach which integrates all theoretically distinguishable ear postures. Four main ear postures were identified: horizontal ears (P posture); ears risen up (R posture); ears pointed backward (B posture); and asymmetric posture (A posture). In a second experiment, we studied how these ear postures were affected by specific emotional states elicited by exposing sheep to experimental situations in which elementary characteristics (ie suddenness and unfamiliarity, negative contrast and controllability) were manipulated. We found that: (i) the horizontal P posture corresponds to a neutral state; (ii) sheep point their ears backward (B posture) when they face unfamiliar and unpleasant uncontrollable situations, hence likely to elicit fear; (iii) they point their ears up (R posture) when facing similar negative situations but controllable, hence likely to elicit anger; and (iv) they expressed the asymmetric A posture in very sudden situations, likely to elicit surprise. By cross-fostering psychological and ethological approaches, we are able to propose an interpretation of ear postures in sheep relative to their emotions.

The study of emotions in animals can be approached thanks to a framework derived from appraisal theories developed in cognitive psychology, according to which emotions are triggered when the individual evaluates challenging events. This evaluation is based on a limited number of criteria such as the familiarity and the predictability of an event. If animals are able to experience emotions rather than simply displaying reflex responses to their environment, then their appraisal of events should, as in humans, modulate their emotional responses. We tested this hypothesis by comparing vocalisations, feeding behaviour, and the startle and cardiac responses of lambs submitted to a sudden event that could or could not be predicted. Lambs able to predict the sudden event thanks to a light cue (associative predictability) showed weaker suddenness-induced startle and cardiac responses and spent more time feeding than their counterparts, thus supporting the existence of an emotional experience in these animals. Furthermore, lambs submitted to the regular appearance of the sudden event (temporal regularity) vocalised less and left less unconsumed food deliveries than lambs submitted to random appearances of the sudden event (controls). These results underline that the cognitive abilities of animals should be taken into account when assessing their emotional experiences and more generally their mood states, which are underlying factors of animal welfare.

Le bien-etre animal implique l'etat physique mais egalement l'etat mental. Les theories de l'evaluation en psychologie cognitive offrent un cadre conceptuel pour etudier le vecu emotionnel de l'animal qui est infere de l'evaluation qu'il fait de la situation a laquelle il est confronte, de ses reponses comportementales et physiologiques. Les criteres en fonction desquels les animaux evaluent leur environnement doivent etre connus afin d'en deduire les emotions qu'ils pourraient ressentir. Nous avons montre que les ovins evaluent un evenement en fonction : 1) de sa previsibilite ; 2) de son adequation avec les attentes prealablement construites ; 3) de la possibilite qu'ils ont de le controler ; 4) du contexte social (dominance/subordination) dans lequel il se produit. Ainsi, les ovins pourraient ressentir des emotions negatives telles que la peur, la colere, ou l'ennui, et des emotions positives comme le plaisir