Could scenarios and models of biodiversity tipping points and human well-being become a transformative lever?
Patricia Howard, Wageningen University and University of Kent
Background and justification
The tipping points (or critical threshold) concept has only recently been directly linked to the term ‘biodiversity’ (Howard 2013). The concept of biodiversity tipping points is generally closely allied to a social-ecological systems perspective, where it is thought that there are a number of key variables and dynamics that have a determining role in the organization of an ecosystem. Within any given system, there are alternative stable states (or ‘stability regimes’) (Scheffer et al. 2001). Beyond some limit, if there even minor changes in the system, it can move across a threshold (or ‘tipping point’) into an alternate stable. Not all ecosystem tipping points are closely related to biodiversity, but it appears that a large majority are, even though it is not always species diversity that plays a key role - it may be species abundance or only a few functionally important species. Scheffer’s (2009) work on critical transitions addresses many biodiversity-related tipping points, including climate-vegetation feedbacks through albedo effects and transpiration, small-scale transitions in semi-arid vegetation, forest-climate feedbacks in boreal regions, formation of raised bogs, species extinctions in fragmented landscapes, and epidemics.
Three types of relations can be involved: biodiversity change is driven by exogeneous drivers (e.g. climate change); there are feedbacks between biodiversity change and an exogeneous driver (e.g. vegetation-climate feedbacks), and biodiversity change is the direct driver, e.g. deforestation, invasive species, or overhunting leading to trophic collapse. An assessment of the vulnerability of Australian ecosystems to tipping points (Laurance et al. 2011) identified four intrinsic features of the most vulnerable ecosystems that relate directly to the species composition of these ecosystems: the history of habitat fragmentation, reliance on ecosystem engineers, reliance on framework species, and reliance on predators or keystone mutualists. Of the environmental threats, five are related to climate change; one to pollution, and the rest to biodiversity change (habitat reduction, habitat fragmentation, changed fire regimes, invasives, overexploitation, and pests and pathogens). Leadley et al. (2010: 8) concluded that major biodiversity transformations will occur at levels near or below a low level of only 2°C global warming, including ‘widespread coral reef degradation, large shifts in marine plankton community structure especially in the Arctic ocean, extensive invasion of tundra by boreal forest, destruction of many coastal ecosystems, etc.’ Potential biodiversity regime shifts that have been identified include: the Amazon forest, the African Sahel, island ecosystems, the tundra, coastal terrestrial systems and sea-level rise, marine fisheries, and tropical coral reefs.
Scenarios and models to address biodiversity tipping points and human well-being
There is a pressing and as yet nearly completely unmet need to assess the vulnerabilities of different people to the pain and suffering, loss of livelihoods and, indeed, of life, associated with potential and real biodiversity-related tipping points. For example, a 2011 World Bank report concerned with a possible regime shift in the Amazon called for ‘a full account of losses…a better valuation of the financial and natural capital represented by the Amazon ecosystem is required as well as a more comprehensive assessment of the economic implications of its potential dieback’ (Vergara & Scholtz 2011: 63). Their concern, however, was not for the impacts on human beings, but for ‘economic losses,’ ‘financial and natural capital’, ‘yields’, and so forth. What might be anticipated for human well-being in the region? Some possible outcomes that might be derived from the World Bank study include:
- The livelihoods base of many indigenous forest peoples (perhaps majority of the 349 ethnic groups) might collapse, which could lead to their virtual disappearance;
- There would be loss of much non-indigenous agriculture, fisheries, and forest industries and thus loss or collapse of self-sufficient production as well as rural employment and investments in the areas worst affected;
- Rural populations would be regionally displaced in order to continue to fish, farm, and harvest forests;
- Rural-urban migration could occur on a mass scale;
- There could be chronic water, food, and energy shortages in urban areas affecting nearly all populations but particularly the majority, who are poor;
- High unemployment in urban areas could result from direct and indirect loss of economic activities, including tourism;
- National and regional level economic crisis could result from loss of export revenues, rising social insecurity, and attempts to substitute for lost ecosystem services;
- There could be increasing conflict, violence, and social instability at sub-national, national, and even inter-basin levels;
- Unemployment and displacement could result in high levels of migration to other nations and continents.
The implications for human well-being beyond the region might not be limited to the ramifications for downstream and upstream markets and employment (e.g. timber, soya, meat, minerals, etc.) and the regional and global financial system, or to the effects of international migration flows or national and regional conflicts (Howard 2013).
Scenarios and models as levers:
Whether or not such scenarios closely or remotely reflect our possible futures, there are very strong reasons to develop them carefully and systematically based upon our best current knowledge, and for policy makers and for the public to pay close attention. Had scientists neglected to make clear the potential consequences of nuclear war for humanity and the types of devastation that were implied, it is possible that such a war would not have been averted until now. Knowing the implications for human suffering and for the future of the human species (e.g. from a possible nuclear winter) has been of inestimable importance in mobilizing public and political support on all sides of the political spectrum to limit nuclear weapons and avoid even limited nuclear warfare.
Scientists and policy makers have to date largely failed to develop models or scenarios that fully consider human well-being and the social, economic, and political repercussions of possible biodiversity tipping points. Discussing this with global environmental change modelling experts showed that they do not consider that this is possible – social phenomena are, in their estimation, too amorphous, and data is unavailable; models would have to be too complex. However, demographers, economists, sociologists, and other social scientists have numerous tools such as agent based models, simulation and other modelling techniques that could certainly be used to these ends. IBPES is committed to scenario and model building (Deliverable 3) – ‘Science should be used to anticipate change – such as the loss of habitats, invasive alien species and climate shifts – to reduce the negative impacts on people and to help us make use of important opportunities.’
At the same time, there are very important measures that we must begin to take with equal seriousness at local scales. Adaptation to local scale tipping points can have very major repercussions not only for regional and global level environmental change and equity, but as well for human resilience in the face of local, regional, and global tipping points of all sorts. The first requirement of any analysis of biodiversity tipping points is to characterize and understand the types of dependencies or inter-dependencies that different human population groups have with rare or fragile species; cultural, ecosystem or economic keystone species; specific trophic levels; specific functional groups, and with specific ecosystem services. The second is to deal with the question of how people are might adapt or maladapt to such phenomenon. Tipping points do not occur overnight. Many ecosystems, trophic levels, etc. are already crossing thresholds toward alternative states; others are manifesting ‘early warnings’ (e.g. slower recovery from perturbations, increasing variance, increasing autocorrelation, flickering, and increased spatial coherence) (Schefffer et al. 2001; Scheffer 2009). Early warnings related to biodiversity tipping points are being identified (de Oliveira Roque et al. 2018), and early warning indicators of biodiversity change in relation to local livelihoods in small island states have been developed to assess the vulnerability of the rural poor, the status of resources important to nutrition, food and medicine, and for access and benefit sharing, among others (Teelucksingh & Perrings 2010). Thus, the only missing ingredients is the committment to developing scenarios and models that simultaneously address biodiversity tipping points and human well-being.
Sub-questions for the scoping assessment:
- Can scenarios and models be developed that consider the possible human implications of crossing tipping points in the Amazon forest, the African Sahel, island ecosystems, or tropical coral reefs that could support transformative change before these tipping points are reached?
- Can such scenarios be used as levers to change perceptions, values and beliefs, and mobilise public opinion toward transformative change? Would adding the ‘human dimension’ to models of biodiversity tipping points finally drive home how significant these changes are for humanity at local, regional, and global scales?
- Can scenarios and models be used to promote the development of early ‘social-ecological’ warning systems?
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Scheffer, M. 2009. Critical Transitions in Nature and Society. Princeton, NJ: Princeton University Press.
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Teelucksingh, S.S. & Perrings, C. 2010. Biodiversity Indicators, Ecosystem Services and Local Livelihoods in Small Island Developing States (SIDS): Early Warnings of Biodiversity Change. Nairobi: UNEP.
Vergara, W. & Scholz, S.M. (eds). 2011. Assessment of the Risk of Amazon Dieback, World Bank Study. Washington DC: The World Bank.