Earth’s ecosystems may be careering toward collapse much sooner than scientists thought, a new study of our planet’s warming climate has warned.
According to the study (Simon Willcock, Gregory S. Cooper, John Addy & John A. Dearing, Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers, Nature Sustainability, Published: 22 June 2023), more than a fifth of the world’s potentially catastrophic tipping points — such as the melting of the Arctic permafrost, the collapse of the Greenland ice sheet and the sudden transformation of the Amazon rainforest into savanna — could occur as soon as 2038.
In climatology, a “tipping point” is the threshold beyond which a localized climate system, or “tipping element,” irreversibly changes. For instance, if the Greenland ice sheet were to collapse, it would also reduce snowfall in the northern part of the island, making large parts of the sheet irretrievable.
Yet the science behind these dramatic transformations is poorly understood and often based on oversimplified models. Now, the new attempt to understand their inner workings has revealed that they may happen much sooner than we thought.
“Over a fifth of ecosystems worldwide are in danger of collapsing,” co-author Simon Willcock, a professor of sustainability at Bangor University in the U.K., said in a statement. “However, ongoing stresses and extreme events interact to accelerate rapid changes that may well be out of our control. Once these reach a tipping point, it’s too late.”
Unlike the well-established link between the burning of fossil fuels and climate change, the study of tipping points is a young and contentious science.
The Intergovernmental Panel on Climate Change (the United Nations’ most important body for evaluating climate science) said in its most recent report that the Amazon rainforest could reach a tipping point that will transform it into a savannah by 2100.
The researchers conducting the study say this prediction is too optimistic.
According to the researchers, most tipping-point studies build the math in their models to focus on one predominant driver of collapse, for example deforestation in the Amazon rainforest. However, ecosystems aren’t contending with just one problem but rather a swarm of destabilizing factors that compound one another. For example, the Amazon also faces rising temperatures, soil degradation, water pollution and water stress.
To investigate how these elements interact and whether these interactions can, in fact, hasten a system’s demise, the scientists built computer models of two lake and two forest ecosystems (including one which modeled the collapse of civilization on Easter Island) and ran them more than 70,000 times while adjusting the variables throughout.
After testing their systems across multiple modes — with just one cause of collapse acting, with multiple causes acting and with all of the causes plus the introduction of random noise to mimic fluctuations in climate variables — the scientists made some troubling findings: multiple causes of collapse acting together brought the abrupt transformation of some systems up to 80% closer to the present day.
And even when the main cause of collapse was not allowed to increase with time, 15% of the collapses occurred purely because of the new elements.
“Our main finding from four ecological models was that ecosystems could collapse 30-80% earlier depending on the nature of additional stress,” co-author John Dearing, a professor of physical geography at Southampton University in the U.K. told Live Science in an email. “So if previous tipping points were forecast for 2100 (i.e. 77 years from now) we are suggesting these could happen 23 to 62 years earlier depending on the nature of the stresses.”
This means that significant social and economic costs from climate change might come much sooner than expected, leaving governments with even less time to react than first thought.
“This has potentially profound implications for our perception of future ecological risks,” co-author Gregory Cooper, a climate systems researcher at the University of Sheffield in the U.K., said in the statement. “While it is not currently possible to predict how climate-induced tipping points and the effects of local human actions on ecosystems will connect, our findings show the potential for each to reinforce the other. Any increasing pressure on ecosystems will be exceedingly detrimental and could have dangerous consequences.”
The study report said:
A major concern for the world’s ecosystems is the possibility of collapse, where landscapes and the societies they support change abruptly. Accelerating stress levels, increasing frequencies of extreme events and strengthening intersystem connections suggest that conventional modelling approaches based on incremental changes in a single stress may provide poor estimates of the impact of climate and human activities on ecosystems.
The scientists conducted experiments on four models that simulate abrupt changes in the Chilika lagoon fishery, the Easter Island community, forest dieback and lake water quality—representing ecosystems with a range of anthropogenic interactions. Collapses occur sooner under increasing levels of primary stress but additional stresses and/or the inclusion of noise in all four models bring the collapses substantially closer to today by ~38–81%. We discuss the implications for further research and the need for humanity to be vigilant for signs that ecosystems are degrading even more rapidly than previously thought.
It said:
For many observers, UK Chief Scientist John Beddington’s argument that the world faced a ‘perfect storm’ of global events by 20301 has now become a prescient warning. Recent mention of ‘ghastly futures’, ‘widespread ecosystem collapse’ and ‘domino effects on sustainability goals’ tap into a growing consensus within some scientific communities that the Earth is rapidly destabilizing through ‘cascades of collapse’. Some even speculate on ‘end-of-world’ scenarios involving transgressing planetary boundaries (climate, freshwater and ocean acidification), accelerating reinforcing (positive) feedback mechanisms and multiplicative stresses. Prudent risk management clearly requires consideration of the factors that may lead to these bad-to-worst-case scenarios. Put simply, the choices we make about ecosystems and landscape management can accelerate change unexpectedly.
The potential for rapid destabilization of Earth’s ecosystems is, in part, supported by observational evidence for increasing rates of change in key drivers and interactions between systems at the global scale. For example, despite decreases in global birth rates and increases in renewable energy generation, the general trends of population, greenhouse gas concentrations and economic drivers (such as gross domestic product) are upwards — often with acceleration through the twentieth and twenty-first centuries. Similar non-stationary trends for ecosystem degradation imply that unstable subsystems are common. Furthermore, there is strong evidence globally for the increased frequency and magnitude of erratic events, such as heatwaves and precipitation extremes. Examples include the sequence of European summer droughts since 2015, fire-promoting phases of the tropical Pacific and Indian ocean variability and regional flooding, already implicated in reduced crop yields and increased fatalities and normalized financial costs.
The increased frequency and magnitude of erratic events is expected to continue throughout the twenty-first century. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report concludes that ‘multiple climate hazards will occur simultaneously, and multiple climatic and non-climatic risks will interact, resulting in compounding overall risk and risks cascading across sectors and regions’. Overall, global warming will increase the frequency of unprecedented extreme events, raise the probability of compound events and ultimately could combine to make multiple system failures more likely. For example, there is a risk that many tipping points can be triggered within the Paris Agreement range of 1.5 to 2 °C warming, including collapse of the Greenland and West Antarctic ice sheets, die-off of low-latitude coral reefs and widespread abrupt permafrost thaw. These tipping points are contentious and with low likelihood in absolute terms but with potentially large impacts should they occur. In evaluating models of real-world systems, we therefore need to be careful that we capture complex feedback networks and the effects of multiple drivers of change that may act either antagonistically or synergistically. Prompted by these ideas and findings, we use computer simulation models based on four real-world ecosystems to explore how the impacts of multiple growing stresses from human activities, global warming and more interactions between systems could shorten the time left before some of the world’s ecosystems may collapse.
The report said:
Intuitively, stronger interactions between systems may be expected to increase the numbers of drivers of any one system, change driver behaviour and generate more system noise. As a result, we would anticipate that higher levels of stress, more drivers and noise may bring forward threshold-dependent changes more quickly. For any particular system (for example, the Amazon forest) it is possible to envisage a time sequence that starts with one main driver (for example, deforestation), then multiple drivers (for example, deforestation plus global warming), more noise through extreme events (for example, more droughts and wildfires), with additional feedback mechanisms that enhance the drivers (for example, diminished internal water cycle and more severe droughts). A vortex could therefore emerge, with drivers generating noisier systems as climate variability and the incidence of extreme events increases. Under worst-case scenarios, the circle becomes faster as reinforcing feedbacks accelerate connections or human activities increase stress levels. However, extreme events could also counteract each other (for example, extreme droughts and extreme rainfall events) and interconnections could also have weakening effects (for example, where increased plant growth driven by increased CO2 is counterbalanced by increased temperatures and droughts. To date, there is limited observational evidence showing that ecosystems have a record of tipping between alternate stable states.
Others offer a mathematical tripartite classification of critical transitions that includes slow driver bifurcations, rate-induced (fast/cumulative driver) and noise-induced (extreme event) tipping points. However, previous studies tend to focus on each of these categories individually. For example, there is a well-established body of physics and mathematical theory on ‘mean exit times’, with studies investigating the timing of tipping points in rate-induced or noisy systems. However, despite calls for more experimental evidence of the impacts of climate variability and extremes on ecosystems, the relative importance or combined effect of fast drivers, multiple drivers and noisy system drivers on the collapse of real-world ecosystems is not known. Critical transitions driven by current pollution forcings such as greenhouse gas emissions and nutrient loadings are likely to be new, well beyond the envelope of natural variability. Hence, we avoid the use of the terms critical transition and tipping points, used formally in dynamical systems theory to represent shifts to alternative attractors and focus on abrupt threshold-dependent changes (ATDCs) that would be perceived by society as the quantitative (for example, fish and stock integrity) and/or qualitative (for example, ecosystem functions) collapse of a desirable system state.
The scientists selected a range of system dynamic models that have been previously used to demonstrate generalizable findings (for example, with regard to safely overshooting ATDCs) and can be externally manipulated to simulate internal emergent ATDCs at local and regional scales—as if they were impacted through stronger connections to other systems. Reflecting modern ecosystems, these models show varied anthropogenic interactions, ranging from social-ecological systems with strongly coupled human–nature feedbacks to ecological systems with predominantly one-way interactions where ecosystems are influenced by the external impacts of people. The ability of these models to capture feedback loops, delays and interactions between components is well established and has motivated their use in various recent studies of sustainability and resilience.
Therefore, the scientists aimed to generalize the dynamics of increasing the numbers of drivers, their rates and variability (as proxies for stronger interactions between systems and noise) on the speed at which ATDCs are reached in four ecosystem dynamics models (Fig. 1): Lake Chilika lagoon fishery, Easter Island, Lake Phosphorus and a modified version of The Hadley Centre Dynamic Global Vegetation Model (TRIFFID) of forest dieback.
Fig. 1: Schematic overview of the framework developed to explore the influence of slow driver trajectories and/or noise on the timing of ATDCs.
a, The four systems models simulated in this study (see section on Overview of systems models). b, Schematic representation of a system dynamics model (Lake Phosphorus model) with its external slow (blue and green) and noisy (red/orange) drivers depicted in colour (see Generation of future scenarios). c, Depiction of the four experiment types (section on Generation of future scenarios), ranging from changes in the primary baseline driver only (experiment 1), changes in all slow drivers and noise inputs simultaneously (experiment 4, where ‘a’ and ‘b’ represent noise profiles that are uncoupled or coupled to the primary driver trajectory, respectively): darker colours schematically represent steeper trajectories and/or higher noise levels. d, The two linear techniques used to check whether outcomes shift into a functionally different state (section on Time-series breakpoint detection)—the top panel is applied to Lake Chilika, Easter Island and TRIFFID, where the systems collapse from high quantitative outcome states to low quantitative outcome states and the bottom panel is applied to Lake Phosphorus (where lake phosphorus concentrations shift from low to high). e, Depiction of the time-series breakpoint date recognition (section on Time-series breakpoint detection). The Easter Island icon in a is made by Roundicons and the remaining three icons are made by Freekpik, as sourced from www.flaticon.com.
The report said:
Previous findings have supported the idea that Earth’s subsystems may interact to the extent that an abrupt shift in one raises the probability that a shift may occur in another.
We have explored, through four ecosystem models, how these interactions may alter the timing of ATDCs through the effects of strengthened drivers, multiple drivers and higher internal variability or noise. The potential effects are substantial with combinations of a strengthened main driver, an additional driver and noise giving at least 38–81% reductions in the future date of a predicted ATDC compared to estimates for a non-interacting system with a constant single driver and no noise. Importantly, the effect per unit time on bringing forward an ATDC is greatest at low driver trajectories, which further strengthens the suggestion that abrupt Earth system changes may occur sooner than we think. Our findings also show that 1.2–14.8% of ATDCs can be triggered by additional drivers and/or noise below the threshold of driver strengths required to collapse the system if only a single driver were in effect.
The study report said:
Whilst our findings derive from models based on real-world systems, the greater complexity of reality may limit the transferability of our results.
