Engaging with the future: framings of adaptation to climate change in conservation

The term ‘adaptation’ is commonplace in conservation research and practice, but often without a reflection on the assumptions, expectations, or frames of reference used to define goals and actions. Communities of practice (e.g. conservation researchers, protected areas managers) have different interpretations of climate change impacts on biodiversity and different ways of defining, operationalizing and implementing adaptation. Their cognitive and motivational expectations for the future are associated with different paths to reach such desired futures. To understand how adaptation is framed in conservation, we undertook a systematic review with a thematic synthesis of the definitions of the term as used in the academic conservation literature. From a sample of 150 articles, only 36 provided a definition of adaptation. We critically appraised the explicit definitions to identify emergent themes that represent particular adaptation approaches. Themes were then grouped, and each group was assigned to a scholarly tradition, onto-epistemological approach and theoretical perspective. Based on theoretical perspectives on social change, we propose a framework (including individual cognitive basis, social interactions, and openness to alternatives) to analyse how change is framed in the definitions and how the framings influence adaptation options. The grouped themes represent passive, active, or indirect adaptation approaches. We used these themes to generate a conceptual model to guide conservation researchers and practitioners engaged in climate adaptation research, policy and management to aid reflection and understanding of the options available to design adaptation agendas and allow negotiation of diverse interests, views and expectations about the future.

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams. Digital technology use in agriculture and agrifood systems research accelerates the production of multidisciplinary data, which spans genetics, environment, agroecology, biology, and socio-economics. Quality labeling of data secures its online findability, reusability, interoperability, and reliable interpretation, through controlled vocabularies organized into meaningful and computer-readable knowledge domains called ontologies. There is currently no full set of recommended ontologies for agricultural research, so data scientists, data managers, and database developers struggle to find validated terminology. The Ontologies Community of Practice of the CGIAR Platform for Big Data in Agriculture harnesses international expertise in knowledge representation and ontology development to produce missing ontologies, identifies best practices, and guides data labeling by teams managing multidisciplinary information platforms to release the FAIR data underpinning the evidence of research impact. The deployment of digital technology in Agriculture and Food Science accelerates the production of large quantities of multidisciplinary data. The Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture harnesses the international ontology expertise that can guide teams managing multidisciplinary agricultural information platforms to increase the data interoperability and reusability. The CoP develops and promotes ontologies to support quality data labeling across domains, e.g., Agronomy Ontology, Crop Ontology, Environment Ontology, Plant Ontology, and Socio-Economic Ontology.

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