Bending the curve of biodiversity loss requires a 'satnav' for nature

Abstract

Georgina Mace proposed bending the curve of biodiversity loss as a fitting ambition for the Convention on Biological Diversity. The new Global Biodiversity Monitoring Framework (GBMF) may increase the chances of meeting the goals and targets in the Kunming-Montreal Global Biodiversity Framework (KMGBF), which requires bending the curve. To meet the outcome goals of KMGBF, the GBMF should support adaptive policy responses to the state of biodiversity, which in turn requires a 'satnav' for nature. The twin pillars of such a satnav are (i) models to predict expected future outcomes of today's choices; and (ii) rapid feedback from monitoring to enable course corrections and model improvement. These same elements will also empower organizations to ensure that their actions are truly nature-positive, but they are not yet written into the GBMF. Without a satnav, society will effectively have to try to find its way to the outcome goals by looking in the rear-view mirror that the current headline indicators provide. Drawing contrasts and parallels with climate modelling, I discuss challenges for indicators, models, data and research culture that must be overcome if we are to bend the curve, and suggest ways forward.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.

Keywords: CBD; IPBES; biodiversity indicators; biodiversity models; biodiversity monitoring; global biodiversity framework.

Figure 1.

Elements of a satnav for nature that links indicators, goals, targets and policies. The elements shown in black correspond to designing a framework that is expected to meet the specified outcome goals for biodiversity, through iterative formulation of action targets and assessment of their model-projected consequences. Elements in grey show how post-implementation monitoring of both biodiversity and drivers, closely linked to ongoing modelling, allows rapid model improvement, model validation and—more importantly—adaptive policy adjustment to reach the outcome goals, if new indicator forecasts indicate that these will not be met.