The regime shifts database (RSDB) is to my knowledge the largest repository of information about regime shifts in the world. It synthesises scientific literature about known social-ecological regime shifts that can have impacts on ecosystem services. I always think of it as a Wikipedia of regime shifts: a public online repository where people can go and learn what are they about, but also contribute content and cases.
Lead by Oonsie Biggs and Garry Peterson at the Stockholm Resilience Centre, the database started as an internship I did as master student in 2009. Later it became my master thesis and soon after the core of my doctoral studies. Several master thesis have been published using the database and it has also been useful for international assessments of regime shifts. It contributed to Global Biodiversity Outlook 3 (2010), the Arctic Resilience Assessment (2016), and more recently I was asked to contribute a short synthesis for IPBES – the International Panel for Biodiversity and Ecosystem Services.
Despite its importance, at least on my eyes, the regime shift database is largely unknown to the scientific community. There is no published peer reviewed paper describing how it was done and how can it be used. As a result, few groups have been publishing work suggesting that a framework for comparing regime shifts is needed, in other words: reinventing the wheel. The first public available draft of that paper is included on my Licenciate thesis (2013), and a later edited pre-print was posted on bioRxive (2015) that was included on my PhD thesis. I needed to graduate. Unfortunately, it has never been submitted for peer-review and people keep using the database without knowing how to give proper recognition to our work. The draft has been sitting on Oonsie’s desk for literally years waiting for the submit button to be pressed.
To date the database describes 30 generic types of regime shifts and 324 case studies, plus a few experimental cases that if included add up to 35 regime shift types. A generic type is for example the coral reefs transitions from coral dominated reefs to macro-algae or other alternative stable states; while a case study would be the coral transitions in Jamaica. A generic type is a collection of all drivers and mechanisms that can produce a particular regime shift, while a case is an instance of its occurrence. An analogy that often helps me distinguish them is the one of a disease (generic type) and the patient (case). Such was my enthusiasm with this project that out of the 30 regime shifts currently available, 16 were contributed by me during the first year of my PhD. Most case studies were contributed by my friend and colleague Johanna Yletyinen (@jo_yletyinen) who spent a lot of time digitalising the Hypoxia database made available by the study of Diaz and Rosenberg in Science (2008). The rest of the contributions have been developed in the class room with master students or researchers who have been generous to share their work with us by filling up a data template. Roughly 1000 scientific publications have been reviewed to build the database.
Last week writing the synthesis for IPBES made me think that we need better visualisations of the RSDB. I created a map where large dots represent generic types or regime shifts. The are located on the kind of ecosystem where they would be expected to occur. So for example, fisheries collapse is located on the southern ocean close to Antarctica (a place where many fish stocks have been reported on be in decline), but a case study about salmon appears on the other side of the planet in Alaska. Case studies are the small dots on the same colour as their generic types. You can see all the hypoxia cases that Johanna coded especially on the coasts of Europe, North America and Japan. Creating the map brought memories of my days writing regime shifts reviews for the database. It also made me realise that it has been few years I have not contributed besides my teaching duty. Disappointed by the lack of commitment with the paper, my PhD student enthusiasm almost disappeared. The last draft I worked on was on desertification (2014?), it was never reviewed nor published online. Each contribution to the RSDB has been peer reviewed by an expert on the topic to make sure we do a fair assessment of the literature. Here is the map:
Currently I live in the United States, Princeton to be precise. It’s not a secret for anyone who follow the news -or try to avoid them like me- that the political situation here is concerning, to say the least. The kind of situation when you ask yourself what can I do? How do I contribute to make this a better place. Scientist being censored, immigration policies that not only contradict the USadian heritage of immigrants but also remind us of a one of the darkest moments of human history. Whatever I could think of seems insignificant.
Then I looked the map again and tried to remember what was my driver when contributing to RSDB. It was making knowledge free for others to be used, the kind of thing that doesn’t contribute to your career but hopefully is doing some good somewhere. If you come from a place such as the one I come from, you know that scientific knowledge is locked behind paywalls and most of people do not have access to scientific literature. Documenting change in ecosystems and making available to a broader public was important to me. It was like sending wealth to someone else, no on the form of money but knowledge, making sure it’s available when is needed, for free. A lot of people is marching for life and standing up for science. My way of contributing will be by putting dots on the map, making scientific facts count and visible when needed. From now on every week I will contribute a case to RSDB. Let’s make science count. If you want to contribute just drop me a line.
For all those struggling with choosing parallel sessions in the Resilience 2014 conference, here are the abstract and slides of my talk for the Marine Regime Shift off site session. Without the animations and video doesn’t look that nice, but I hope it still gets the message across.
Marine regime shifts have typically been studied through statistical signature of jumps in state variables as response on changing drivers. They are caused by multiple drivers, making identification elusive and controversial. Instead of looking in retrospective whether they occurred or what caused them, we use a comparative analysis of marine regime shifts to identify patterns of driving processes. Tripartite networks are modelled to study which drivers are more likely to interact and which bundles of ecosystem services are more commonly affected. Our simulations show that driver interactions differ from random. The main drivers that produce marine regime shifts are climate forcing, nutrients inputs and fishing. Driver interactions often alter biophysical processes such as upwelling; while indirect drivers often connect land and ocean dynamics. Regime shifts might be masked by fast variables such as trade, high response diversity of functional groups, or fast dynamics of lower trophic levels. Masking variables can also mitigate the impact of regime shifts on ecosystem services such as fisheries and food security. Our analysis suggest that marine regime shift could be synchronized in time given the drivers they share, but also, that the occurrence of certain regime shifts could increase the likelihood of others. Integrated management of marine regime shifts is needed to avoid cascading regime shifts; by simultaneously addressing multiple drivers, several regime shifts can be avoided. However, managerial strategies are likely to fail if they are limited to direct drivers, and fail to consider indirect drivers, masking effects or stochastic events.
In my master thesis (2010) started exploring the ideas of domino effects across regime shifts. Although the idea is cool, attractive and hard to avoid, it’s hard to break down to formal theory and even worst to test empirically. This post from Scientific American (March 15, 2013) best summarises the state of the art in the debate. It only focus on climate tipping points, but the reader can easily extend it to non-climatic ones, such as the global collapse scenario proposed by the HANDY model recently developed with NASA support (news from the Guardian), where the tipping points rest on inequality.
From here down is copied from Scientific American website, I just find it inspiring:
Quick-Change Planet: Do Global Climate Tipping Points Exist?
Is there a chance that human intervention—rising temperatures, massive land-use changes, biodiversity loss and so on—could “tip” the entire world into a new climatic state? And if so, does that change what we should do about it?
As far back as 2008 NASA’s James Hansen argued that we had crossed a “tipping point” in the Arctic with regard to summer sea ice. The diminishing ice cover had moved past a critical threshold, and from then on levels would drop precipitously toward zero, with little hope of recovery. Other experts now say that recent years have confirmed that particular cliff-fall, and the September 2012 record minimum—an astonishing 18 percent lower than 2007’s previous record—was likely no fluke.
Sea ice represents a big system, but it is generally thought to be self-contained enough to follow such a tipping-point pattern. The question that has started to pop up increasingly in the last year, however, is whether that sort of phase transition, where a system shifts rapidly—in nonlinear fashion, as scientists say—from one state to another without recovery in a timescale meaningful to humanity, is possible on a truly global scale.
“You’re pushing an egg toward the end of the table,” says Tony Barnosky, a professor at the University of California, Berkeley. At first, he says, “not much happens. Then it goes off the edge and it breaks. That egg is now in a fundamentally different state, you can’t get it back to what it was.” Barnosky was the lead author on a much-discussed paper in Nature[DL1] last summer that suggested the world’s biosphere was nearing a “state shift”—a planetary-scale tipping point where seemingly disconnected systems all shifted simultaneously into a “new normal.” (Scientific American is part of Nature Publishing Group.)
Claims of catastrophic temperature shifts are unlikely to go down without an argument. A new paper published recently in Trends in Ecology & Evolution by Barry Brook of the University of Adelaide in Australia and colleagues argues that there is no real grounding to the idea that the world could display true tipping-point characteristics. The only way such a massive shift could occur, Brook says, is if ecosystems around the world respond to human forcings in essentially identical ways. Generally, there would need to be “strong connections between continents that allow for rapid diffusion of impacts across the planet.”
This sort of connection is unlikely to exist, he says. Oceans and mountain ranges cut off different ecosystems from each other, and the response of a given region is likely to be strongly influenced by local circumstances. For example, burning trees in the Amazon can increase CO2 in the atmosphere and help raise temperatures worldwide, but the fate of similar rainforests in Malaysia probably depend more on what’s happening locally than by those global effects of Amazonian deforestation. Brook and colleagues looked at four major drivers of terrestrial ecosystem change: climate change, land-use change, habitat fragmentation and biodiversity loss; they found that truly global nonlinear responses basically won’t happen. Instead, global-scale transitions are likely to be smooth.
“To be honest, when others have said that a planetary critical transition is possible [or] likely, they’ve done so without any underlying model,” Brook says. “It’s just speculation…. No one has found the opposite of what we suggested—they’ve just proposed it.” In their analysis Brook’s group concluded that the diversity of local responses to global forcings like increasing temperature means we cannot identify any particular point of no return.
Tim Lenton, an expert on tipping points at the University of Exeter in England, says there is no convincing evidence of global shift yet, but he doesn’t rule out the possibility. “It’s not obvious how you can get a change in Siberia then causing a synchronized change in Canada or Alaska,” he says, referring to a commonly cited climate feedback loop of permafrost melting at northern latitudes. “That doesn’t seem likely. It’s more that different parts of the Arctic are going to reach the thawing threshold at the same time just because they’re getting to the same kind of temperature.” This is a fine distinction: Are we looking at multiple systems tipping as one, or just a coincidental amalgam of unconnected systems falling off a ledge at similar time points?
Lenton says that there is a chance that ice-free Arctic summers could start a cascade effect—for example, elevated temperatures on nearby land that eventually find their way down into the permafrost and cause rapid melting. The carbon released by the permafrost could in turn initiate further warming, and maybe tip another disconnected system and so forth. “It’s a bit like having some dominos lined up,” he says. “I’m not sure yet whether we have a scenario like that for future climate change, but it’s worth consideration.”
Such a domino effect could end up looking more like a “smooth” response than a nonlinear one, but NASA’s Hansen says this doesn’t suggest we should ignore it. “Most tipping points are ‘smoothed out,’ but that does not decrease their importance,” he says. Even Arctic sea ice shows a smoothed response as it rolls past the point of no return. “Once you have passed a certain point, it takes only little additional forcing to lose all the sea ice.” And he echoes Lenton on the idea of dominos and hugely important sub-global systems: “[It’s] hard to see how the Greenland ice sheet would survive if we have sea ice-free summers.” In other words, melted sea ice could beget massive sea level rise, thanks to a supposedly unconnected system.
And further, that non-connectivity is not necessarily a given. Barnosky argues that the fundamental assumption that systems around the world are not strongly connected is no longer true. “What used to be isolated parts of the Earth really are very connected now, and we’re the connectors,” he says. Further, his group’s paper based the possibility of a global tipping point largely on comparisons to planetary history: Earth has exhibited rapid phase shifts in the past, and we are blowing those types of changes out of the water now. For example, the shift from the last glacial period into the current interglacial, which took only a few millennia ending around 11,000 years ago, featured abrupt land-use change: about 30 percent of the land surface went from ice-covered to ice-free over those few thousand years. In just a few hundred years, humans have converted about 43 percent of the world’s land to agricultural or urban landscapes.
Whether such rapid changes portend a new global shift is, to some extent, an esoteric, academic question. The answer depends on whether the world can really follow the classic mathematical definition of tipping points that relies on “bifurcation theory.” That theory holds that a system follows a smooth curve until a certain threshold—the egg rolls at similar speed until it hits the edge of the table—when it jumps to a new state with no obvious change in external pressures. And importantly, once that jump is made there is essentially no going back; you can’t “unbreak” the egg.
And at the bottom of the mathematical debate is a question of utility: Would the existence of a real planetary-scale tipping point change how we should confront our environmental challenges, from energy sources to land use?
A more accurate picture would not just let us prepare for rapid climate change, but might help us predict it as well. Marten Scheffer, of Wageningen University in the Netherlands, has done extensive work on ways we can see tipping points coming. On smaller scales, he says, a system can exhibit “critical slowing down”—a slowed ability to recover from perturbations—before jumping to the irreversible new state. Scheffer says, arguing for tipping points, that past global-scale, quick changes in climate appear to have exhibited a similar effect.
And if we agree a tipping point can exist, maybe we can even try and stave it off. As the world seems to be inching closer to addressing climate change, identifying specific targets for the most effective mitigation grows ever more important. In his recent State of the Union speech, Pres. Barack Obama called for unilateral action to address global warming–related emissions; if we can find a tipping point threshold, is that reason to adjust such action to reflect the possibility of rapid global-scale change?
“If there is plausibility to one of these tipping points, which I think there is, then it’s an even more urgent matter to act to slow all of these individual stressors down,” U.C. Berkeley’s Barnosky says, “because the outcomes could be more surprising and more disruptive to society, and happen faster than we have time to react…. I’d much rather err on the side of precaution then ignore the possibility of tipping points and then be unpleasantly surprised when they happen.”
Next week I’m going to Planet Under Pressure conference in London. On monday I will be presenting recent work on modeling two-mode networks (bipartite) to understand which regime shifts drivers co-occur more often than expected. I want to understand which combination of tipping points are more likely to interact when looking at global trends of regime shifts.
Here is the abstract submitted and the poster (hi-res pdf) for those that won’t make it among the hundreds of posters presented.
Dynamic Tipping Points: Which Ones Are More Likely to Interact?
Over the past 50 years humans have changed ecosystems faster and more extensively than in any other comparable period in the past 1, 2. These changes have contributed to various global syndromes 3 or regime shifts: abrupt, non-linear changes in social-ecological systems that can severely impact the flux of ecosystem services human societies rely upon4, 5.
Inspired by recent work on the application of relational networks to human diseases 6, 7, this paper explores the patterns of relationships among driving causes of regime shifts. Networks analysis offers a perspective for analysing regime shifts interactions in contexts where (i) time series data needed for early warnings and (ii) detailed information on causal mechanisms needed for models is limited or unavailable. Bipartite or two-mode networks are analysed based on information collected in the Regime Shifts Database (www.regimeshifts.org). Relations among drivers of change are studied for 20 regime shifts in marine, terrestrial and polar ecosystems. These regime shifts impact multiple ecosystem services, including provisioning services such as food (freshwater, crops and fish); regulating services such as water purification; as well as cultural services such as aesthetics and recreation. Regime shifts are a multi-causal phenomena; but preliminary results show that global drivers of regime shifts tend to interact in non-random patterns. Agriculture-related activities, global warming, biodiversity loss and economic drivers are the main causes of regime shifts independently of scale or ecosystem type.
A research framework is proposed to approach regime shifts management in high uncertainty settings which uses recent theoretical progress on network controllability8 to explore the analysis to causal mechanisms, feedback loop strength and cascading effects among regime shifts.
key words: regime shifts, network analysis, dynamic tipping points
1. Rockström, J., et al. (2009) A safe operating space for humanity. Nature 461, 472-475
2. Steffen, W., et al. (2007) The Anthropocene: Are humans now overwhelming the great forces of nature. Ambio 36, 614-621
3. Schellnhuber, H., et al. (1999) Syndromes of Global Change. Gaia 6, 19-34
4. Scheffer, M., and Carpenter, S. (2003) Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 18, 648-656
5. Scheffer, M., et al. (2001) Catastrophic shifts in ecosystems. Nature 413, 591-596
6. Barrenas, F., et al. (2009) Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies. PLoS ONE 4, e8090 EP –
7. Goh, K.-I., et al. (2007) The human disease network. Proceedings of the National Academy of Sciences 104, 8685-8690
8. Liu, Y., et al. (2011) Controllability of Complex Networks. Nature
The Guardian publish some days ago a set of pictures documenting the efforts of NGO’s and local communities to fight desertification in Mongolia. They wrote:
Inner Mongolia, China’s third largest province, is battling severe desertification. Over-grazing, logging, expanding farms and population pressure, as well as droughts, have turned once fertile grasslands into sandy plains. As part of China’s efforts to stop the land degradation, NGOs have been helping with reforestation
See all pictures here: Desertification in Inner Mongolia, China – in pictures | Global development