Exploring the empirical evidence on biocultural diversity: a systematic map protocol for papers written in English and Spanish

ABSTRACT

Background

The term biocultural diversity (BCD) refers to the diversity of life in all its manifestations, including biological, cultural, and linguistic diversity, and the interactions among them within a socio-ecological system. As human societies interact with the natural world in unique ways, BCD recognizes the link between biodiversity and their knowledge, beliefs, practices, and values. However, the challenge now is on how to improve human livelihoods without harming nature in a context where global change is threatening both ecosystems and human well-being. Biocultural approaches aim to merge social sciences and natural sciences in order to bridge the divide between disciplines and try to solve the sustainability issues we are facing. Nevertheless, to achieve that, the BCD concept still requires some clarification, indicators for its quantification, and more integration with the social-ecological systems (SES) science.

We present a protocol to systematically map BCD and explore how this concept has been empirically addressed in scientific research articles written in English and/or Spanish. This systematic map aims to highlight research gaps, create a spatial database of evidence, point out actions that have been taken through biocultural approaches and provide evidence to inform future decision-making processes.

Methods

We will search a suite of bibliographic databases for relevant research literature. We will screen identified and retrieved articles at two stages (1. Title/abstract and 2. Full-text), according to a predefined set of inclusion/exclusion criteria. The consistency of these criteria will be checked in order to ensure that they can be operationalised.

We will extract detailed information on the BCD concept and the study and practical application of this concept as a tool for addressing environmental and sustainability issues in social-ecological systems, as well as descriptive information about the articles. We will visualise this database on an Evidence Atlas, identifying knowledge gaps and clusters by using descriptive analysis.