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Increasing evidence-synthesized in this paper-shows that economic growth contributes to biodiversity loss via greater resource consumption and higher emissions. Nonetheless, a review of international biodiversity and sustainability policies shows that the majority advocate economic growth. Since improvements in resource use efficiency have so far not allowed for absolute global reductions in resource use and pollution, we question the support for economic growth in these policies, where inadequate attention is paid to the question of how growth can be decoupled from biodiversity loss. Drawing on the literature about alternatives to economic growth, we explore this contradiction and suggest ways forward to halt global biodiversity decline. These include policy proposals to move beyond the growth paradigm while enhancing overall prosperity, which can be implemented by combining top-down and bottom-up governance across scales. Finally, we call the attention of researchers and policy makers to two immediate steps: acknowledge the conflict between economic growth and biodiversity conservation in future policies; and explore socioeconomic trajectories beyond economic growth in the next generation of biodiversity scenarios.

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Population models in ecology are often not good at predictions, even if they are complex and seem to be realistic enough. The reason for this might be that Occam's razor, which is key for minimal models exploring ideas and concepts, has been too uncritically adopted for more realistic models of systems. This can tie models too closely to certain situations, thereby preventing them from predicting the response to new conditions. We therefore advocate a new kind of parsimony to improve the application of Occam's razor. This new parsimony balances two contrasting strategies for avoiding errors in modeling: avoiding inclusion of nonessential factors (false inclusions) and avoiding exclusion of sometimes-important factors (false exclusions). It involves a synthesis of traditional modeling and analysis, used to describe the essentials of mechanistic relationships, with elements that are included in a model because they have been reported to be or can arguably be assumed to be important under certain conditions. The resulting models should be able to reflect how the internal organization of populations change and thereby generate representations of the novel behavior necessary for complex predictions, including regime shifts.

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Mega-sites have a notable impact on surrounding ecological systems. At such sites there are substantial risks associated with complex socio-ecological interactions that are hard to characterize, let alone model and predict. While the urge to control and clean-up mega-sites (control and correct) is understandable, rather than setting a goal of cleaning up such sites, we suggest a more realistic response strategy is to address these massive and persistent sources of contamination by acknowledging their position as new features of the socio-ecological landscapes within which they are located. As it seems nearly impossible to clean up such sites, we argue for consideration of a 'coping with change' rather than a 'control and correct' approach. This strategy recognizes that the current management option for a mega-site, in light of its physical complexities and due to changing societal preferences, geochemical transformations, hydrogeology knowledge and remedial technology options may not remain optimal in future, and therefore needs to be continuously adapted, as community, ecology, technology and understanding change over time. This approach creates an opportunity to consider the relationship between a mega-site and its human and ecological environments in a different and more dynamic way. Our proposed approach relies on iterative adaptive management to incorporate mega-site management into the overall socio-ecological systems of the site's context. This approach effectively embeds mega-site management planning in a triple bottom line and environmental sustainability structure, rather than simply using single measures of success, such as contaminant-based guidelines. Recognizing that there is probably no best solution for managing a mega-site, we present a starting point for engaging constructively with this seemingly intractable issue. Therefore, we aim to initiate discussion about a new approach to mega-site management, in which the complexity of the problems posed by mega-sites is reflected upon in its entirety. These complexities are associated with uncertainties and unknowns that have to be addressed, as they have an impact on the strategies being developed and applied. We contend that the best that can be hoped for in mega-site management is an acceptable solution for the current state of affairs, with good flexibility to modify strategies as new site conditions, remediation possibilities, community preferences and management objectives develop over time.

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