In the video below Tim Daw, from the University of East Anglia’s School of International Development and the Stockholm Resilience Centre, explains his project Participatory Modelling of Wellbeing Tradeoffs in Coastal Kenya. The project, in which I’m also participating, has examined tradeoffs between social wellbeing and ecological conservation in small scale fisheries in Kenya using a combination surveys, models, scenarios, and participatory workshops.
For more information on the project is available on the Stockholm Resilience Centre’s website. The project is funded by the UK’s Ecosystem Services and Poverty Alleviation programme. and there is more information on the ESPA website.
For more on poverty and ecosystem service tradeoffs see:
Daw, T., Brown, K., Rosendo, S. & Pomeroy, R. 2011. Applying the ecosystem services concept to poverty alleviation: the need to disaggregate human well-being. Environmental Conservation, 38, 370–379. DOI: 10.1017/S0376892911000506
Raudsepp-Hearne, C., Peterson, G.D. & Bennett, E.M. 2010. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. Proceedings of the National Academy of Sciences of the United States of America, 107, 5242–7. doi: 10.1073/pnas.0907284107
In many developing countries, an individual household will often engage in a range of economic sectors, such as fishing, farming, and tourism. These diverse ‘livelihood portfolios’ are thought to help to spread risk and make households more resilient to shocks in a particular sector. Whether and how local people engage in multiple occupations has important implications for how people use and manage natural resources and is of particular relevance to people involved in managing natural resources. But for scientists, donors, and policy makers, unraveling the complexity of livelihoods in developing countries has been extremely challenging.
In our recent paper in PLoS ONE, we developed a novel method for exploring complex household livelihood portfolios. We used a network-based approach to examine how the role of natural resource-based occupations changes along spectra of socioeconomic development and population density in 27 communities across 5 western Indian Ocean countries (see Fig. 1).
Figure 1. Kenyan livelihood landscape maps at various scales of social organization: a) Shela, Kenya; b) an aggregation of peri-urban sites in Kenya; c) an aggregation of rural sites in Kenya; d) all sites in Kenya.
In Figure 1 the links between occupations are indicated by arrows. The size of a node indicates the relative involvement in that occupational sector (larger node means more people are involved). The direction of the arrows indicates the priority of ranking. Thus an arrow into an occupation indicates that the occupation was ranked lower than the occupation the arrow came from. The thickness of the arrows corresponds to the proportion of households being engaged in the, by themselves, higher ranked occupation that are also engaged in the lower ranked occupation. The proportion of the node that is shaded represents the proportion of people that ranked that occupation as a primary occupation.
an increase in household-level specialization with development for most (but not all) occupational sectors, including fishing and farming, but that at the community-level, economies remained diversified.
We also found that households in less developed communities often share a common occupation, whereas that patterns is less pronounced in more developed communities. This may have important implications for how people both perceive and solve conflicts over natural resources.
Finally, our network-based approach to exploring livelihood portfolios can be utilized for many more types of analyses conducted at varying scales, ranging from small villages to states and regions.
Roxanne Maranger an ecologist at the University of Montreal and other have a neat paper in Nature Geoscience Nitrogen transfer from sea to land via commercial fisheries that shows that commercial fishing removed substantial amounts of nitrogen from coastal oceans. They show that while fertilizer run-off into the ocean and fishery removal of nitrogen have increased over the past forty years, the increase in nitrogen inputs has been faster. Consequently the proportion of nitrogen removed from coastal zone has dropped from a global average of about 60% in 1960 to about 20% in 2000. This trend as well as the spatial pattern of nitrogen withdrawal are shown in figure 1 of their paper:
Figure 1. a, Total amount of N in fertilizer run-off (Tg N yr-1=1012 g N yr-1) delivered to the global ocean (left axis, blue line) and N returned as fish biomass (left axis, red line) per year over time. The orange line (right axis) is the proportion of fish N removed relative to fertilizer N exported (ratio fish N:fertilizer N) reported as a percentage. b, The ratio of fish N removed to fertilizer N entering 58 different large marine ecosystems (LMEs) for the year 1995.
The paper shows that fishing can help reduce the impacts of nitrogen pollution. But that nitrogen pollution that destroys fisheries, through the creation of anoxic “dead zones”, can make nitrogen pollution even worse by removing a major source of nitrogen withdrawals. Similarly, overfishing the reduces the amount of fish biomass that can be removed from a system will make the system more vulnerable to eutrophication.