Intertwined people-nature relations are central to nature-based adaptation to climate change

Abstract

Adaptation to climate change is a social-ecological process: it is not solely a result of natural processes or human decisions but emerges from multiple relations within social systems, within ecological systems and between them. We propose a novel analytical framework to evaluate social-ecological relations in nature-based adaptation, encompassing social (people-people), ecological (nature-nature) and social-ecological (people-nature) relations. Applying this framework to 25 case studies, we analyse the associations among these relations and identify archetypes of social-ecological adaptation. Our findings revealed that adaptation actions with more people-nature relations mobilize more social and ecological relations. We identified four archetypes, with distinct modes of adaptation along a gradient of people-nature interaction scores, summarized as: (i) nature control; (ii) biodiversity-based; (iii) ecosystem services-based; and (iv) integrated approaches. This study contributes to a nuanced understanding of nature-based adaptation, highlighting the importance of integrating diverse relations across social and ecological systems. Our findings offer valuable insights for informing the design and implementation of adaptation strategies and policies.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: archetype; co-production; ecosystem services; framing; pathway; social-ecological system.

Figure 1.

Analytical framework to assess social–ecological relations in the nature-based adaptation to climate change.

Figure 2.

Scatterplots representing the associations between PP, NP and NN relation scores across the case studies. Regression lines are drawn only where the correlation coefficient is significantly different from zero. Colours refer to the cluster in which a case has the highest membership (figure 4).

Figure 3.

Factor analysis outputs showing the major loadings on each of the six factors (in italic text). All loadings with an absolute value above a cut-off point of 0.25 are represented as a path.

Figure 4.

Clustering results. The scatterplot (left) is a principal component analysis biplot showing the 25 case studies (circles, whose colours indicate clusters with membership above 0.25), the four cluster centroids (stars), the four envelopes drawn around the cases belonging to only one cluster and the centroid, and the six factors (arrows with labels). The bottom-right plot shows the mean values of the six factors in the clusters.