Actors engaging in integrated landscape approaches to reconciling conservation and development represent multiple sectors and scales and actors with different powers, resource access, and influence on decision-making. Despite growing acknowledgement, limited evidence exists on the implications of power relations for landscape governance. Therefore, this paper asks why and how different forms of power unfold and affect the functioning of multi-stakeholder platforms in southern Zambia. Social network analysis and a power influence assessment reveal that all actors exercise some form of visible, hidden, or invisible power in different social spaces to influence decision-making or negotiate a new social order. The intersection of customary and state governance reveals that power imbalances are the product of actors’ social belongingness, situatedness, and settlement histories. We conclude that integrated landscape approaches are potentially suited to balance power by triggering new dynamic social spaces for different power holders to engage in landscape decision-making. However, a power analysis before implementing a landscape approach helps better recognise power differentials and create a basis for marginalised actors to participate in decision-making equally. The paper bears relevance beyond the case, as the methods used to unravel power dynamics in contested landscapes are applicable across the tropics where mixed statutory and customary governance arrangements prevail.
Tag: Network analysis
Policy learning as complex contagion: how social networks shape organizational beliefs in forest-based climate change mitigation
Policy learning can alter the perceptions of both the seriousness and the causes of a policy problem, thus also altering the perceived need to do something about the problem. This then allows for the informed weighing of different policy options. Taking a social network perspective, we argue that the role of social influence as a driver of policy learning has been overlooked in the literature. Network research has shown that normatively laden belief change is likely to occur through complex contagion—a process in which an actor receives social reinforcement from more than one contact in its social network. We test the applicability of this idea to policy learning using node-level network regression models on a unique longitudinal policy network survey dataset concerning the Reducing Deforestation and Forest Degradation (REDD+) initiative in Brazil, Indonesia, and Vietnam. We find that network connections explain policy learning in Indonesia and Vietnam, where the policy subsystems are collaborative, but not in Brazil, where the level of conflict is higher and the subsystem is more established. The results suggest that policy learning is more likely to result from social influence and complex contagion in collaborative than in conflictual settings.
Soil macroinvertebrate communities: A world-wide assessment
AimMacroinvertebrates comprise a highly diverse set of taxa with great potential as indicators of soil quality. Communities were sampled at 3,694 sites distributed world-wide. We aimed to analyse the patterns of abundance, composition and network characteristics and their relationships to latitude, mean annual temperature and rainfall, land cover, soil texture and agricultural practices.LocationSites are distributed in 41 countries, ranging from 55° S to 57° N latitude, from 0 to 4,000 m in elevation, with annual rainfall ranging from 500 to >3,000 mm and mean temperatures of 5–32°C.Time period1980–2018.Major taxa studiedAll soil macroinvertebrates: Haplotaxida; Coleoptera; Formicidae; Arachnida; Chilopoda; Diplopoda; Diptera; Isoptera; Isopoda; Homoptera; Hemiptera; Gastropoda; Blattaria; Orthoptera; Lepidoptera; Dermaptera; and “others”.MethodsStandard ISO 23611-5 sampling protocol was applied at all sites. Data treatment used a set of multivariate analyses, principal components analysis (PCA) on macrofauna data transformed by Hellinger’s method, multiple correspondence analysis for environmental data (latitude, elevation, temperature and average annual rainfall, type of vegetation cover) transformed into discrete classes, coinertia analysis to compare these two data sets, and bias-corrected and accelerated bootstrap tests to evaluate the part of the variance of the macrofauna data attributable to each of the environmental factors. Network analysis was performed. Each pairwise association of taxonomic units was tested against a null model considering local and regional scales, in order to avoid spurious correlations.ResultsCommunities were separated into five clusters reflecting their densities and taxonomic richness. They were significantly influenced by climatic conditions, soil texture and vegetation cover. Abundance and diversity, highest in tropical forests (1,895 ± 234 individuals/m2) and savannahs (1,796 ± 72 individuals/m2), progressively decreased in tropical cropping systems (tree-associated crops, 1,358 ± 120 individuals/m2; pastures, 1,178 ± 154 individuals/m2; and annual crops, 867 ± 62 individuals/m2), temperate grasslands (529 ± 60 individuals/m2), forests (232 ± 20 individuals/m2) and annual crops (231 ± 24 individuals/m2) and temperate dry forests and shrubs (195 ± 11 individuals/m2). Agricultural management decreased overall abundance by 54% in tropical areas and 64% in temperate areas. Connectivity varied with taxa, with dominant positive connections in litter transformers and negative connections with ecosystem engineers and Arachnida. Connectivity and modularity were higher in communities with low abundance and taxonomic richness.Main conclusionsSoil macroinvertebrate communities respond to climatic, soil and land-cover conditions. All taxa, except termites, are found everywhere, and communities from the five clusters cover a wide range of geographical and environmental conditions. Agricultural practices significantly decrease abundance, although the presence of tree components alleviates this effect.