WikiRate.org - Leveraging collective awareness to understand companies' environmental, social and governance performance

Abstract. WikiRate is a Collective Awareness Platform for Sustainability and Social Innovation (CAPS) project with the aim of \crowdsourcing better companies" through analysis of their Environmental Social and Governance (ESG) performance. Research to inform the design of the platform involved surveying the current corporate ESG information landscape, and identifying ways in which an open approach and peer production ethos could be e ffectively mobilised to improve this landscape's fertility. The key requirement identi ed is for an open public repository of data tracking companies' ESG performance. Corporate Social Responsibility reporting is conducted in public, but there are barriers to accessing the information in a standardised analysable format. Analyses of and ratings built upon this data can exert power over companies' behaviour in certain circumstances, but the public at large have no access to the data or the most infuential ratings that utilise it. WikiRate aims to build an open repository for this data along with tools for analysis, to increase public demand for the data, allow a broader range of stakeholders to participate in its interpretation, and in turn drive companies to behave in a more ethical manner. This paper describes the quantitative Metrics system that has been designed to meet those objectives and some early examples of its use.

Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds

In the last thirty years, the emergence and progression of biologging technology has led to great advances in marine predator ecology. Large databases of location and dive observations from biologging devices have been compiled for an increasing number of diving predator species (such as pinnipeds, sea turtles, seabirds and cetaceans), enabling complex questions about animal activity budgets and habitat use to be addressed. Central to answering these questions is our ability to correctly identify and quantify the frequency of essential behaviours, such as foraging. Despite technological advances that have increased the quality and resolution of location and dive data, accurately interpreting behaviour from such data remains a challenge, and analytical methods are only beginning to unlock the full potential of existing datasets. This review evaluates both traditional and emerging methods and presents a starting platform of options for future studies of marine predator foraging ecology, particularly from location and two-dimensional (time-depth) dive data. We outline the different devices and data types available, discuss the limitations and advantages of commonly-used analytical techniques, and highlight key areas for future research. We focus our review on pinnipeds - one of the most studied taxa of marine predators - but offer insights that will be applicable to other air-breathing marine predator tracking studies. We highlight that traditionally-used methods for inferring foraging from location and dive data, such as first-passage time and dive shape analysis, have important caveats and limitations depending on the nature of the data and the research question. We suggest that more holistic statistical techniques, such as state-space models, which can synthesise multiple track, dive and environmental metrics whilst simultaneously accounting for measurement error, offer more robust alternatives. Finally, we identify a need for more research to elucidate the role of physical oceanography, device effects, study animal selection, and developmental stages in predator behaviour and data interpretation.