Simple methods for improving the communication of uncertainty in species' temporal trends. (August 2022)

• Record Type: Journal Article
• Authors: Pescott, O.L.; Stroh, P.A.; Humphrey, T.A.; Walker, K.J.
• Is Part Of: Ecological indicators. Volume 141(2022)
• Publication Date: 2022-08
• Subjects: Information visualisation -- Environmental monitoring -- Risk-of-bias -- Line ensembles -- Model-based inference -- Stakeholder communication -- Bootstrap -- Bayesian model; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.109117
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Results from information visualisation can be used to improve trend and indicator communication. Line ensembles and discretised summaries can be used to better display model-based uncertainty. Broader uncertainty relating to risk-of-bias should also be displayed for full transparency. Abstract: Temporal trends in species occupancy or abundance are a fundamental source of information for ecology and conservation. Model-based uncertainty in these trends is often communicated as frequentist confidence or Bayesian credible intervals, however, these are often misinterpreted in various ways, even by scientists. Research from the science of information visualisation indicates that line ensemble approaches that depict multiple outcomes compatible with a fitted model or data may be superior for the clear communication of model-based uncertainty. The discretisation of continuous probability information into frequency bins has also been shown to be useful for communicating with non-specialists. We present a simple and widely applicable approach that combines these two ideas, and which can be used to clearly communicate model-based uncertainty in species trends (or composite indicators) to stakeholders. We also show how broader ontological uncertainty can be communicated via trend plots using risk-of-bias visualisation approaches developed in other disciplines. The techniques are demonstrated using the example of long-term plant distributional change in Britain, but are applicable to any temporal data consisting of averages and associated uncertainty measures. Our approach supports calls for full transparency in the scientific process by clearly displaying the multiple sources of uncertainty that can be estimated by researchers.