Vári, Podschun, Erős et al. (2022): Freshwater systems and ecosystem services: Challenges and chances for cross-fertilization of disciplines.” Ambio. – 51(2022)

Abstract:

Freshwater ecosystems are among the most threatened in the world, while providing numerous essential ecosystem services (ES) to humans. Despite their importance, research on freshwater ecosystem services is limited. Here, we examine how freshwater studies could help to advance ES research and vice versa. We summarize major knowledge gaps and suggest solutions focusing on science and policy in Europe. We found several features that are unique to freshwater ecosystems, but often disregarded in ES assessments. Insufficient transfer of knowledge towards stakeholders is also problematic. Knowledge transfer and implementation seems to be less effective towards South-east Europe. Focusing on the strengths of freshwater research regarding connectivity, across borders, involving multiple actors can help to improve ES research towards a more dynamic, landscape-level approach, which we believe can boost the implementation of the ES concept in freshwater policies. Bridging these gaps can contribute to achieve the ambitious targets of the EU’s Green Deal.

Freshwater systems and ecosystem services: Challenges and chances for cross-fertilization of disciplines | SpringerLink

Aminpour et al. (2020): Wisdom of stakeholder crowds in complex social–ecological systems

Payam Aminpour, Steven A. Gray, Antonie J. Jetter, Joshua E. Introne, Alison Singer and Robert Arlinghaus

Abstract:

Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations.
We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge. Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the best scientific understanding. We show that the averaged model from a crowd of diverse resource users outperforms those of more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive, yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of non-scientist stakeholders.

https://doi.org/10.1038/s41893-019-0467-z