# Maintaining human wellbeing as socio-environmental systems undergo regime
shifts

Andrew R. Tilman  USDA Forest Service, Northern Research Station Elisabeth H.
Krueger  Institute for Biodiversity and Ecosystem Dynamics, University of
Amsterdam  High Meadows Environmental Institute, Princeton University  Lisa C.
McManus  Hawaii Institute of Marine Biology, University of Hawaii at Manoa
James R. Watson  College of Earth, Ocean and Atmospheric Sciences, Oregon
State University

(September 8, 2023)

###### Abstract

Global environmental change is pushing many socio-environmental systems
towards critical thresholds, where ecological systems’ states are on the
precipice of tipping points and interventions are needed to navigate or avert
impending transitions. Flickering, where a system vacillates between
alternative stable states, is touted as a useful early warning signal of
irreversible transitions to undesirable ecological regimes. However, while
flickering may presage an ecological tipping point, these dynamics also pose
unique challenges for human adaptation. In this work, we link an ecological
model that can exhibit flickering to a model of human adaptation to a changing
environment. This allows us to explore the impact of flickering on the utility
of adaptive agents in a coupled socio-environmental system. We highlight the
conditions under which flickering causes wellbeing to decline
disproportionately, and explore how these dynamics impact the optimal timing
of a transformational change that partially decouples wellbeing from
environmental variability. The implications of flickering on nomadic
communities in Mongolia, artisanal fisheries, and wildfire systems are
explored as possible case studies. Flickering, driven in part by climate
change and changes to governance systems, may already be impacting
communities. We argue that governance interventions investing in adaptive
capacity could blunt the negative impact of flickering that can occur as
socio-environmental systems pass through tipping points, and therefore
contribute to the sustainability of these systems.

_Keywords_ Social-ecological systems $\cdot$ Critical transitions $\cdot$
Early-warning signals $\cdot$ Wellbeing $\cdot$ Flickering

## Introduction

Global change impacts, including those resulting from climate change and
socioeconomic transitions, are altering the environment and threatening human
livelihoods. For example, worsening drought conditions lead to an increased
risk of wildfires (McKenzie et al., , 2004), and global mean sea-level rise
threatens the habitability of low-elevation coastal zones (Vitousek et al., ,
2017). In ecological systems, these environmental changes are linked to
phenomena known as regime shifts, wherein there is a “large persistent change
in the structure and function of an ecosystem” (Biggs et al., , 2012).
Prominent case studies include shifts from productive coral-dominated reefs to
degraded systems dominated by macroalgae (Mumby et al., , 2007), and shifts
from a highly vegetated to a barren state in arid landscapes (Rietkerk et al.,
, 2004). Because these transitions have implications for the ability for
humans to thrive in these systems, much attention has been focused on
identifying indicators to serve as ‘early warning signals’ for impending
catastrophic changes (Scheffer et al., , 2009; Bauch et al., , 2016). Whether
informed by early warning signals or not, people experiencing ecological
regime shifts can attempt to adapt to these changing conditions to maintain
their wellbeing. These adaptation measures could include minor changes to
practices such as switching target species in fisheries (Katsukawa and
Matsuda, , 2003), or more major changes such as migration to pursue an
alternative livelihood elsewhere (Adamo, , 2010).

While identifying and attempting to avoid a transition to an undesirable
alternative state remains a challenge, in this paper we focus on how people
navigate ecological regime shifts. Adapting to environmental change while
maintaining wellbeing poses unique challenges, especially in noisy systems.
The global extent of human societies demonstrates the ability of people to
adapt to (and prosper under) a broad range of ecological regimes. Other
organisms also demonstrate this adaptive capacity in their evolutionary
response to environmental change (Carlson et al., , 2014). However, in social
and environmental systems, stochasticity can result in a phenomenon known as
“flickering” when the system approaches a regime shift (Taylor et al., , 1993;
Wang et al., , 2012; Gatfaoui and De Peretti, , 2019). Flickering describes
how a system switches between alternative stable states as a result of
stochasticity. In the context of an impending regime shift, flickering leads
to periods of time which resemble the status quo alternating with times
defined by a novel socio-environmental state. This presents a unique challenge
for adaptive agents: which regime should one adapt to and when should one
shift practices to align with the expected post-regime-shift environment?
There may be cases where agents themselves flicker between alternative
livelihoods in an attempt to adapt to the intermittent shifts in environmental
regimes. Our results suggest that in systems where people have limited
adaptive capacity, flickering can yield marked declines in utility.

Figure 1: Illustrations showing how alternative stable environmental states
can be conceptualized as basins where a ball subject to stochastic
perturbations (shown by arrows) may settle, as illustrated in the first panel.
Noise may cause the environmental state to tip from a low to a high potential
state or vice versa, a process termed flickering. In agricultural systems
there are often a range of technologies / approaches / strategies that have
environmentally dependent utility curves (3 are illustrated in the lower
panel). As environmental states shift, the most favorable strategy changes. In
this paper, we consider the case where a continuum of environmentally
dependent utility curves exist and individuals adapt to environmental change
by shifting the peak of their utility curve in an attempt to track changing
environmental conditions. We show that environmental regimes with flickering
pose unique challenges for adaptive agents and can lead to troughs in average
utility.

The importance of adaptation to flickering and nonlinear ecological dynamics
can be illustrated through environmentally dependent utility functions. Such
utility functions can arise when they depend on environmental productions
functions. These functions provide a mapping between some measure of the
environmental state and production or output. They are typically approximated
by hump-shaped functions (Schlenker and Roberts, , 2009) (see Figure 1b). In
agriculture, for example, a production function for a particular crop may be
dependent on temperature. Multiple functions with peaks that span different
ranges of the environmental state (along the x-axis) represent production
under different strategies. In this context, climate adaptation can be thought
of as people shifting their production strategy, and thereby transitioning
across the different production functions in order to maintain high levels of
output despite environmental change. In agriculture, this could be achieved by
choosing different varieties of crops as temperature increases.

However, these production functions do not explicitly account for the non-
linear dynamics associated with coupled social-ecological systems. Whereas
average temperature in a region may slowly increase in response to climate
change and lead to the expectation of a steady advance through a series of
production strategies, underlying environmental conditions that shape
productivity may exhibit far more complex dynamics in response to gradual
global change. To integrate these effects, we consider an environmental model
that has the potential for nonlinear dynamics in response to gradual change in
an underlying parameter. These nonlinear dynamics associated with changes in
the environmental state are often depicted using a well-potential diagram
(Figure 1a). Well-potential diagrams can illustrate how ecosystems (and
social-ecological systems) exhibit alternative stable states, and how the
resilience of a particular state may be eroded by a relatively slow-changing
parameter like average temperature. As the resilience of one basin of
attraction is diminished and an alternative basin arises, flickering can occur
prior to a tipping point being crossed. After the tipping point is crossed the
old basin ceases to exist and the system transitions to the alternative stable
state.

In contrast to production functions that are dependent on a gradually changing
environmental parameter, the utility functions we model depend on an
environment with complex dynamics and can lead to highly stochastic utility as
the environment flickers between alternative stable states and as people
struggle to adapt to this volatility. Additionally, due to the hump-shaped
relationship between the environment and productivity, environmental
variability will tend to depress average productivity due to non-linear
averaging. By integrating the non-linear dynamics of coupled social-ecological
systems – especially the dynamical flickering associated with some regime
shifts – with the economic concept of environmentally-dependent production
functions, we provide new insight into the potential impacts of regime shifts
on human wellbeing. We explore the impact of people’s adaptive capacity on
their ability to track environmental change and maintain their wellbeing. We
also examine when transformational change to novel strategies which buffer
individuals against environmental change should be adopted given flickering
dynamics.

We develop a mathematical model to describe the impact of alternative stable
states and flickering on the utility of adaptive agents in a coupled socio-
environmental system. We use this model to highlight the conditions wherein
flickering has the largest negative impact on people’s wellbeing and explore
how the timing of people’s transitions to new strategies should relate to the
timing to environmental transitions caused by flickering and tipping points.
We discuss several possible case studies that illustrate how these dynamics
could or may be impacting communities, and primarily contextualize our model
based on the response of Mongolian nomadic pastoralists to global change.
Nomadic pastoralist communities have been among the hardest hit by the
consequences of global change. New political regimes have shifted borders,
fundamental changes to economic systems have limited the prospects of nomadic
ways of life, and extended drought periods have put the health of livestock
herds at risk. Using available data from the literature and other publicly
available sources, we discuss how global change impacted these communities,
with long-term effects that include the migration of people away from their
traditional homes. We also discuss how flickering dynamics could have similar
consequences for artisanal fishing communities impacted by marine climate
shocks and communities impacted by wildfires.

## Socio-environmental model

The model has two components: an ecological component where nonlinear dynamics
(and in particular flickering) occur, and a social component, where agents
adapt their production frontier to align with the environment and maximize
their utility.

### Ecological dynamics

In our model, we assume that there is an ecological state, $x$, that can be
described by logistic growth and experiences sigmoidal harvest rate $c$
following a type-3 functional response (Holling, , 1959). This approach forms
the basis for well-studied ecological models that can exhibit alternative
stable states and hysteresis (May, , 1977; Scheffer, , 1989); models of this
form have also been used to study flickering dynamics (Dakos et al., , 2012).
The ecological state, $x$, could represent the abundance of forage plants in
the context of grazing systems, fish biomass in the case of fisheries, or
forest biomass in the case of wildfires. The discrete-time stochastic dynamics
of $x$ are described by

$x_{t+1}=\left(rx_{t}\left(1-\frac{x_{t}}{K}\right)-c\frac{x_{t}^{2}}{x_{t}^{2}+h^{2}}\right)+\left(1+i_{t}\right)x_{t}$
(1)

where $r$ is intrinsic growth rate of $x$, $K$ is its carrying capacity, $h$
is the half-saturation constant (i.e., the resource level at which half of the
maximum extraction rate is reached), and $i_{t}$ a noise term that models
environmental shocks. We assume that $i_{t}$ is time-correlated red noise
governed by

$i_{t+1}=\left(\left(1-\frac{1}{T}\right)i_{t}+\eta_{t}\right),$ (2)

where $i_{t}x_{t}$ is the magnitude of the stochastic reduction or increase of
the resource at time $t$, $T$ is the time scale over which noise becomes
uncorrelated, and $\eta_{t}\sim\mathcal{N}(0,\beta^{2})$ is an element of a
series of independent identically distributed normal error terms.

In the absence of noise (i.e., when $\beta=0$), this system can exhibit a
range of dynamics. For large values of $r$, discrete-time logistic systems
such as this can exhibit cyclic or chaotic dynamics (May, , 1974). To simplify
our analyses, we restrict our attention to those cases where cyclic and
chaotic dynamics do not occur in the absence of noise. For harvesting rates
$c$, that are low, the system has a single stable equilibrium corresponding to
an environmental state of abundance. For high values of $c$, the sole stable
equilibrium is a depleted environmental state. For intermediate values of $c$
there exists a region with multiple stable equilibria. In this intermediate
regime, the inclusion of noise can lead to a flickering dynamic where the
system makes irregular jumps from the high to low environmental basins of
attraction (Dakos et al., , 2012). Figure 2 shows the stable and unstable
equilibrium states across a range of harvesting values, and illustrates that
for intermediate extraction rates, the system has two alternative stable
states.

Figure 2: For low and high extraction rates, the system has only one stable
equilibrium. For intermediate extraction rates, bistability occurs and the
potential for flickering dynamics arises. We use this distinction to classify
our system into three distinct dynamical regimes. In regime 1, only the high
environmental state is stable, regime 2 exhibits bistability and potentially
flickering dynamics, and only the low environmental state is stable in regime
3. For any extraction rate, actual dynamics of the environment will fluctuate
about their equilibria due to stochasticity.

### Human adaptation and wellbeing

To model human wellbeing in response to a constantly changing environment, we
assume that people can adapt their practices to be in alignment with the
environmental state, but that this adaptation process takes time. The rate at
which individuals can adapt to a changing environment depends on their
adaptive capacity. Here, we conceptualize adaptation as individual or
collective actions that allow individuals to be as successful as possible,
given the current state of the environment. Adaptation allows individuals to
shift the peak of their production function so that it aligns with the current
environmental state. Potential avenues for adaptation are myriad and depend on
the context of the case study. For pastoralist systems, they include moving to
better locations when the local resource level is low, implementing irrigation
systems to bolster productivity, and storing feed for cattle (Chen et al., ,
2015). In agriculture, it could include adjusting the timing of planting and
harvesting, and changing (or diversifying) the varieties of crops grown.

We employ a very simple model that captures the notion of adaptive capacity
and allows us to explore the consequences of decreased adaptive capacity on
people’s wellbeing. We let $y_{t}$ be the environmental state to which
individuals are most well adapted. When $y_{t}=x_{t}$, agents achieve the
highest possible payoff given the current environmental state. The adaptation
of individuals to environmental change is constrained by their adaptive
capacity, $l$. When $l=1$, individuals adapt fully in one time step to the
current environmental state. On the other hand, when $l<<1$, it will take much
more time for adaptation to a particular environmental state to be achieved.
We model the dynamic of adaptation as a deterministic discrete-time dynamical
system governed by

$y_{t+1}=l(x_{t}-y_{t})+y_{t}.$ (3)

We construct a utility function that depends on the best possible payoff,
$\pi(x)$, that can be attained given the current environmental state, $x$, and
on the extent to which there is a divergence between the environmental state
and the state to which individuals are most well adapted. We assume that the
highest achievable payoff given an environmental state $x$ is a linearly
increasing function $\pi(x)$, such that the potential for high payoffs
improves with the state of the environment (Figure 3). We let this payoff be
the utility that is achieved by an individual who is perfectly adapted to
state $x$, (i.e. $y(t)=x(t)$). When there is some degree of divergence between
the current environmental state and an individual’s adaptation, then utility
decreases. We assume that utility can be described as a Gaussian function of
environmental adaptation, $y$, given by

$U(x,y)=\pi(x)\;\exp\left(\frac{-\ln(2)(x-y)^{2}}{a^{2}}\right)$ (4)

where $a$ defines the degree of misadaptation at which utility is cut in half
from its peak. This utility function adheres to our assumption that when
environmental adaptation, $y$, is equal to the current environmental state,
$x$, then utility, $U(x,y)$, is equal to the payoff $\pi(x)$.

Figure 3: Illustrations showing two representative cases. In the first panel,
the relationship between the environmental state and maximum payoff, $\pi(x)$
is shown for a case where the payoffs are highly sensitive to the
environmental state (Case 1) and where payoffs vary less in response to
different environmental states (Case 2). The second panel shows the
relationship between the misadaptation to the environment and how much of
payoffs individuals realize as utility. In Case 1, individuals are more
sensitive to misadaptation than Case 2. Finally, in the bottom panel, these
impacts are aggregated and utility is shown as a function of the state most
well adapted to ($y$) for a current environmental state of $x=7$.

### Human-environmental dynamics

The coupled dynamics of the environment and adaptation can be described by the
system of difference equations

$\displaystyle x_{t+1}$
$\displaystyle=\left(rx_{t}\left(1-\frac{x_{t}}{K}\right)-\frac{cx_{t}^{2}}{x_{t}^{2}+h^{2}}\right)+\left(1+i_{t}\right)x_{t}$
(5) $\displaystyle i_{t+1}$
$\displaystyle=\left(\left(1-\frac{1}{T}\right)i_{t}+\eta_{t}\right)$ (6)
$\displaystyle y_{t+1}$ $\displaystyle=l(x_{t}-y_{t})+y_{t},$ (7)

where $i$ is a red noise term, $x$ is the state of the environment, and $y$ is
the environment to which individuals are most well adapted. Figure 2 shows
that for low extraction rates, the sole stable equilibrium of the system is a
high environmental state, but as extraction increases, a tipping point is
crossed and the environmental state collapses. Layered on top of this tipping
point is human adaptation, which influences wellbeing. We start by focusing
solely on the dynamics of the environment and adaptation. Later we will turn
our attention to the implications for wellbeing of these dynamics.

### Results

#### Environmental adaptation in three regimes

Figure 2 identifies three regimes which exhibit qualitatively distinct
environmental dynamics. In regime 1, there is a single stable equilibrium with
high resource biomass. In regime 2, there are alternative stable states, one
with high biomass and one with a degraded environmental state. Lastly, in
regime 3, only the degraded environmental equilibrium remains.

We are motivated by the scenario in which historical conditions of the system
correspond to regime 1. In other words, we start in a scenario where high
biomass predominates. Nevertheless, the system is stochastic, and fluctuation
about this high-biomass equilibrium can be significant in magnitude. The
dynamics of adaptation are governed by the same equation across all three
regimes, with agents adjusting their practices toward the current
environmental state. Figure 4(a) shows that dynamics of the environment in
regime 1 exhibit significant variation but that adaptation generally falls
within the range of environmental variability.

In response to shifting economic structures and environmental change, we
assume that the extraction rate, $c$, in the system will increase over time
and the system will eventually fall within regime 2. Figure 4(b) and Figure
4(c) with $c$ values corresponding to regime 2 show examples of flickering
environmental dynamics. Whereas adaptation largely remains within the range of
environmental variability in regime 1, in regime 2 when the system flips from
one equilibrium region to the other, there are significant time periods where
adaptation is significantly misaligned from the state of the environment. We
show that this has important implications for the utility (wellbeing) of
agents in regime 2.

Figure 4(d) shows a case where the extraction rate is high enough that the
system has been pushed beyond a tipping point and is in regime 3. The only
stable equilibrium is a collapsed environmental state, but stochastic dynamics
may nonetheless lead to ephemeral periods where environmental dynamics
resemble historical conditions. In regime 3, agents are more or less able to
maintain close adaptation to the environment. Because of this, we expect
utility to approach the maximum payoffs that can be attained under perfect
adaptation.

(a) Regime 1: $c=1$ results in a high, but variable environment state

(b) Regime 2: $c=1.95$ shows flickering dynamics with brief periods of
collapse.

(c) Regime 2: $c=2.45$ results in flickering dynamics with longer periods in a
collapsed state.

(d) Regime 3: $c=3.1$ yields system collapse, with the possibility of brief
periods which resemble recovery.

Figure 4: Temporal dynamics of the environment and adaptation across three
regimes. When environmental dynamics fall into regime 1 or 3, individuals
adaption generally falls within the range of day-to-day environmental
variability. For flickering environmental dynamics as seen in regime 2, when
the environment flips from one basin of attraction to another, there are
extended periods where individuals are significantly misadapted to the
environment. This has important implications for wellbeing.

#### Wellbeing and environmental regimes

Wellbeing depends both on the maximum profitability that could be achieved
given environmental conditions, as well as the degree to which agents are
adapted to the environmental state. In this section, we examine how wellbeing
(i.e., utility) depends on the environmental extraction rate, $c$.

As discussed, extraction rates structure the system into three qualitatively
distinct regimes. Figure 5 shows how average payoff assuming perfect
adaptation and average utility depend on which regime the extraction rate
falls within and on the level of adaptive capacity, $l$. Figure 5 shows the
maximum average payoff,

$\overline{\pi}=\frac{1}{t_{\text{max}}}\sum_{t=1}^{t_{\text{max}}}\pi\left(x_{t}\right),$
(8)

that could be achieved through time for different fixed rates, $c$, of
environmental extraction. The figure also represents average utility,

$\overline{U}=\frac{1}{t_{\text{max}}}\sum_{t=1}^{t_{\text{max}}}U\left(x_{t},y_{t}\right),$
(9)

for several levels of adaptive capacity, $l$. Unlike $\overline{\pi}$,
$\overline{U}$ depends on both the state of the environment, $x_{t}$ and
adaptation, $y_{t}$. For higher values of adaptive capacity, $l$, the
qualitative pattern of average utility mirrors that of average payoff. When
$l=0.1$, Both average payoff, $\overline{\pi}$ and average utility,
$\overline{U}$ gradually decline as the extraction rate increases through the
three regimes.

For moderate to low levels of adaptive capacity, (e.g. $l=0.01$ to $l=0.001$)
the qualitative patterns seen in average payoff, and in average utility under
high adaptive capacity no longer hold. In these cases, the flickering dynamics
of regime 2 exacts a costly toll on average utility. The repeated switching
between high and low environmental states leads to extended periods of
misadaptation that diminish average utility and creates a noticeable utility
trough in regime 2.

In regime 3, the collapsed environment doesn’t vary as widely, and even agents
with limited adaptive capacity can eventually adapt their behaviors to the
permanently degraded environmental state. This leads to an increase in the
average utility of agents with low adaptive capacity and a convergence of
utilities and payoffs across all scenarios. This result raises important
questions about the usefulness of flickering as an early warning signal in
socio-environmental systems. Flickering dynamics can depress average wellbeing
by creating highly variable environmental conditions with abrupt shifts that
require significant time to adapt to, especially for agents with low adaptive
capacity.

Our simulations suggest that rather than being an early warning signal, in
socio-environmental systems, flickering can be a uniquely challenging regime
for agents with low adaptive capacity.

Figure 5: Maximum attainable payoff given perfect environmental adaptation and
realized utility under actual environmental adaptation for a range of
extraction rates, $c$ and levels of adaptive capacity, $l$. Flickering can
occur in regime 2 for intermediate extraction rates. Flickering dynamics
present unique challenges for adaptive agents, especially those with limited
adaptive capacity. When agents have low adaptive capacity, flickering leads to
a utility trough that does not occur for agents with high adaptive capacity.

#### Transformational change

In this section we consider the option of transformational change, where
agents can choose once to dramatically overhaul their practices and adopt a
more generalist approach.

In Figure 3 we showed two alternative cases for the structure of the payoff
and utility functions. The first case, where payoffs are highly sensitive to
the environmental state and utility is sensitive to misadaptation, has been
the focus of the preceding simulation results. In essence, we have assumed
that individuals attempt to become specialists in their environment. The
second case shown in Figure 3 highlights an alternative possibility, that
payoffs are less sensitive to environmental conditions and utility is less
sensitive to misadaptation. This corresponds more closely with a generalist
strategy, where peak payoffs may never be as high, but adaptation to precise
environmental conditions is less important.

Transformation to a generalist approach is an alternative to specialist
adaptation to one’s environment. Under transformation, agents fundamentally
alter their practices in order to switch their payoff and utility functions
from those in case 1 to those described by case 2. Figure 6 shows the maximum
average payoff, $\overline{\pi}$, of a specialist approach (case 1, in dark
green) and a transformational generalist approach (case 2, in dark purple). In
this example, the payoff functions are such that if agents were always
perfectly adapted to the environment, they would choose the transformational
approach as the system nears regime 3 and the dark purple circles rise above
the dark green circles. However, agents will not always be perfectly adapted
to the current environment, so their realized average utility will fall below
the maximum attainable average payoff. In this case, agents would increase
their average utility by adopting the transformational approach (case 2) while
the system is still in regime 1. After transformation, the flickering dynamics
experienced in regime 2 are far less detrimental to average utility. After
crossing into regime 3, the average utility of the transformation approach
remains higher than the baseline specialist approach. Other payoff and utility
functions can be constructed where the transformational generalist approach is
only favored during the flickering regime. In this case, analyzing average
payoffs could indicate that transformation is never beneficial, while an
analysis focusing on realized average utility could show that transformation
can help agents navigate flickering critical transitions without suffering as
greatly from the utility trough that might otherwise occur.

Figure 6: If agents are posed with the problem of when to transform their practices from a specialist approach (Case 1) to a generalist approach (Case 2) in response to a steadily increasing extraction rate in the system, the answer will depend on whether they take adaptation into account. Case 1 corresponds to the status quo, where payoffs are highly dependent on the state of the environment and highly sensitive to misadaptation. Case 2 corresponds to a transformative change, where payoffs are less sensitive to the state of the environment and misadaptation. Case 1 resembles a setting where individuals specialize their practices adapted to environmental conditions. Case 2 represents a transformative change where individuals’ wellbeing is less sensitive to the environmental and adaptation states. As the extraction rate increases, agents are eventually better off adopting transformative change. However, the timing of this shift depends on the adaptive capacity of agents. If people are always perfectly adapted to the environment, then payoffs under the status quo remain higher than those under transformation until after the system passes its tipping point at the cusp of regimes 2 and 3. On the other hand, when people are limited in their adaptive capacity, the optimal transformation timing is earlier, as illustrated by the intersection of the pink and green points (Utility curves) in regime 1. In this case, people are better off transforming before the system even reaches the flickering regime. Variable | Range of values | Description
---|---|---
$x_{t}$ | 0–20 | Current environmental state
$y_{t}$ | 0–20 | Current adaptation state
$l$ | .001-1 | Adaptation rate
$i_{t}$ |  | Auto-correlated red noise
$T$ | 30 | Timescale over which noise becomes uncorrelated
$\eta_{t}$ | 0 | i.i.d. normal error term
$\beta$ | .07 |  The standard deviation of $\eta$’s
$r$ | 1 | Resource growth rate
$K$ | 10 | Resource carrying capacity
$c$ | 0–4 | Extraction rate
$h$ | 1 | Extraction half-saturation constant
${\pi}(x)$ | 5–10 | Environmentally dependent payoffs
$U(x,y)$ |  0–$\pi(x)$ | Utility as a function of environmental and adaptation states
$a$ | 3-5 |  Value of $|x-y|$ for which $U(x,y)=1/2\;\pi(x)$
Table 1: Variables and parameters in the model, their approximate range of
values, and meanings. Exact parameter values used for each figure available in
Table SI 1.

## Case studies

### Nomadic pastoral systems in Mongolia

Climate change, land degradation and socio-political disruptions threaten the
livelihoods of tens of millions of nomadic pastoralists who inhabit the semi-
arid areas of Central Asia, the Middle East, North Africa and the Sahel zone.
Nomadic pastoralists move seasonally between several locations that provide
adequate conditions for livestock grazing and water access (Sneath, , 2003).

Mongolia is the worlds most sparsely populated country, has an arid to semi-
arid climate, few forests, and very limited arable land (Mongolian Statistical
Information System, , 2021). Nomadic lifestyles are well adapted to these
highly variable landscapes and climate conditions. In Mongolia, nomadic
pastoralists employ many adaptive strategies including storage of fodder for
use during poor grazing conditions, mobility to seek out better rangelands,
and communal pooling of resources and labor (Fernández-Giménez et al., ,
2015). Today, about one third of the country’s population continues to be
nomadic or semi-nomadic, while two thirds live in urban areas (See figure SI
5).

Over the past 70 years, Mongolia has experienced an increase in average annual
temperatures of 2.1°C (Lkhagvadorj et al., , 2013), which has been accompanied
by the proliferation of extreme weather conditions, in particular since the
mid-1990 (Enebish et al., , 2020). Severe winter weather events called Dzud,
characterized by inaccessible grazing resources, have occurred repeatedly and
with devastating effects for nomadic livelihoods, killing 33, 23 and 10
million heads of cattle in 1999, 2003 and 2010, respectively (Rao et al., ,
2015).

During this time of dramatic environmental change, Mongolia also experienced a
political transition from a socialist to a capitalist regime. This brought
about changes to the governance, institutional, and public service structures
upon which both settled and nomadic people rely. Prior to the emergence of the
capitalist regime in 1990, herders were organized into modular collectives of
families around community centers known as _‘bag’_. These centers provided
technical, social, health and veterinary services, as well as emergency stocks
of fodder to buffer against Dzud events (Fernández-Giménez et al., , 2015).
These centers bolstered the resilience of Mongolian pastoralist communities,
even to severe shocks such as the loss of a family’s entire herd. They
represented type of community insurance that allowed families who lost their
herd to restock the following year (Fernández-Giménez et al., , 2015; Ahearn,
, 2018).

Upon the institution of a capitalist regime, these governance structures were
abandoned. Bag centers and their social services all but disappeared
(Fernández-Giménez et al., , 2015). Consequently, pastoralist families had to
adapt by becoming increasingly self-reliant; they grew their livestock herds
to raise income during favorable years and to be able to buffer losses during
Dzud years. Livestock numbers increased from around 20-25 million during the
socialist era, when livestock numbers were strongly regulated, to around 70
million today (Mongolian Statistical Information System, , 2021), with grazing
pressure growing accordingly (See Supplementary Information, Figure SI 1).

In response to these dynamics and the devastating Dzud losses of the past
decades, external actors, such as the Asian Development Bank, pushed the
Mongolian government to privatize land ownership, in order to give exclusive
land use rights to individual families and reduce overgrazing (Sneath, ,
2003). However, land privatization came at the cost of mobility, an essential
element of pastoralism. Mobility is an adaptation to the low average
productivity and high spatio-temporal variability of Mongolian rangelands
(Sneath, , 2003). Land privatization and reduced mobility led to a further
deterioration of local pastures, adding to the vulnerability of the
pastoralist families (Fernández-Giménez et al., , 2015), (Wang et al., ,
2013). Thus, a number of compounding factors led to maladaptive measures and
the loss of adaptive capacity and wellbeing: The response to extreme weather
conditions and changing governance systems resulted in increased grazing
pressure and may have provoked signals of environmental flickering leading to
massive losses in livestock, while measures promoted by external actors aimed
at tackling the loss of previous governance systems on the one hand, and the
problem of overgrazing on the other hand, proved to be maladaptive.

After the devastating Dzud of 2009/10, a number of donor-initiated community-
based natural resource management organizations were initiated with the aim to
establish community structures that resembled those that existed during the
socialist regime, which had provided adaptive capacity at the community level.
Others responded to the increasingly difficult conditions for maintaining
rural lifestyles by migrating to urban centers. While the rural population has
remained more or less stable over the past thirty years, the urban population
has doubled (Mongolian Statistical Information System, , 2021)(See
Supplementary Information, Figure SI 4). For some, moving to an urban setting
presented new opportunities for education and employment. For others, the
transformation posed insurmountable challenges. Rapid urbanization has
overwhelmed urban governance institutions, and the majority of urban migrants
in Mongolia have inadequate access to basic services, such as water,
sanitation and electricity (Terbish and Rawsthorne, , 2016), and are unable to
support their livelihoods once livestock herds are lost to Dzud or sold during
the rural-urban migration process.

For those that have remained in a (semi-) nomadic lifestyle, there is evidence
of adaption to the highly variable (and potentially flickering) dynamics of
weather conditions and pasture quality through new forms of communal
governance (Fernández-Giménez et al., , 2015), and private insurance (Ahearn,
, 2018). Under these highly unstable conditions, pastoralists have to invest
into costly adaptation measures. A study examining adaptation strategies to
climate impacts in Mongolia found more than 50 adaptation strategies applied
in different combinations and with different frequencies (Wang et al., ,
2013).

While land degradation has even been reversed in some areas of the Mongolian
Plateau (Guo et al., , 2021), many pastures of the Mongolian landscape remain
heavily degraded. A combination of high mobility and collective social support
structures made Mongolia’s semi-nomadic society highly adapted to fluctuating
weather conditions and productivity of grasslands. However, climate and
governance change has tested the resilience of these pastoralist communities.
While results for donor-incentivized community-based resource management have
been mixed, lessons can be learned from successful cases. Herd size
regulations and agreements to share grazing lands among herder families could
help sustain the livelihoods of families with smaller herd sizes (Fernández-
Giménez et al., , 2015). When urbanization is the preferred
adaptation/transformation strategy, governance mechanisms could be established
that facilitate this transformation at an early stage, to avoid extended
losses in utility (and wealth) resulting from continued adaptation to
‘flickering’ environmental conditions. However, given already insufficient
urban infrastructures, sprawling (and under-served) urban settlements and
overwhelmed city management in cities around the world, including Mongolia,
governance actors should carefully consider what types of interventions will
contribute to greater sustainability of local human-environment systems.
Importantly though, given the high cost of adaptation to flickering socio-
environmental conditions, the high cost and resulting loss of utility
associated with (mal-) adaptive efforts must be accounted for when weighing
options for when and how to intervene. Governance interventions into rural and
urban livelihoods may create new interdependencies and dynamics between social
and ecological systems and between rural and urban environments.

### Fisheries

Fisheries support the income and food security of millions of people around
the world (McClanahan et al., , 2015). Fisheries are also being severely
impacted by climate change: from increases in ocean temperatures impacting the
structural integrity of coral reefs and the fisheries they support (Hoegh-
Guldberg et al., , 2017), to ocean acidity impacting early life stages of fish
(Waldbusser et al., , 2015) to species range shifts altering the spatial
distribution of fishing effort (Pinsky and Fogarty, , 2012), many fisheries
are suffering, with subsequent adverse effects on coastal communities
(Hollowed et al., , 2013).

Fishers have developed several ways of dealing with these changes though. In
particular, fishers can move where they go, tracking fish stocks as they shift
with climate change (Selden et al., , 2020, although the price of fuel greatly
constrains where there can go). Switching fisheries and operating in numerous
fisheries over the course of a year is also common. This is one way in which
fishers “smooth” their income over the year. But it can be costly in time and
money, and sometimes impossible given certain fisheries management
institutions (e.g., permits are required to fish, but sometimes not
available). Switching between fisheries also requires training, knowledge and
different fishing gear.

Exposure to an ecological regime shift can restructure the incentives for what
species are targeted. For example, coral reefs around the world are
experiencing more frequent and intense marine heatwaves that lead to coral
bleaching and mass coral mortality events (Hoegh-Guldberg et al., , 2017;
Hughes et al., , 2018). In addition to impacts on coral cover, bleaching
events are altering reef fish assemblages, especially if reefs experience a
shift towards an algal-dominated state (Richardson et al., , 2018; Robinson et
al., , 2019). Return times between bleaching events are presently about every
six years and because coral requires on the order of 10-15 years for the
fastest species to recover (Gilmour et al., , 2013), it is possible that reefs
today are experiencing flickering between a high- vs. low-coral state or have
already transitioned into the latter.

For coral reef fisheries, fishers reduce their sensitivity to climate change,
including the impacts of flickering between high- and low-coral cover states,
through livelihood diversification (Cinner et al., , 2012). For example, in
the Caribbean, alternative livelihoods among coastal fishers include
agriculture, forestry, aquaculture, construction work, and ecotourism
(Karlsson and Mclean, , 2020). However, socio-economic barriers including
poverty, a minimal social safety net, or a lack of access to capital can limit
adaptive capacity and prevent this diversification (Cinner et al., , 2012).
Governance aimed at dismantling these barriers may also provide second-order
benefits by helping to smooth the transition of socio-economic systems though
the flickering stages of ecological regime shifts. The main concern suggested
by our modeling is that if there is flickering between ecological/fishery
states, then fishers might lose income by repeatedly adapting to different
states. In addition to the costs associated with gaining the knowledge, the
institutional costs such as attaining permits for a new fishery, and the sunk-
costs associated with procuring necessary new fishing gear, may force many
fishers to take drastic/transformative action, such as leaving fishing
altogether. All of these factors indicate that a flickering transition in the
underling ecosystem that a fishery is part of, is likely to cause a decline in
wellbeing among fishers, potentially reducing the viability of certain
fisheries in the future.

### Forest management and wildfire risk

Forest management can be viewed as embedded within a socio-environmental
system. The ecological dynamics of forests and the stochastic dynamics of
wildfire are both coupled with management practices including timber
harvesting, fire suppression, and prescribed fire (Luce et al., , 2012;
Steelman, , 2016).

Climate change has led to increasing frequency and severity of drought, as
well as hotter peak summer temperatures in the forest ecosystems of the
Western US (McKenzie et al., , 2004). These changes have coincided with
increasing tree density in western forests which was driven, in part, by a
long-term management emphasis on fire suppression (Fellows and Goulden, ,
2008). Furthermore, there has been a dramatic increase in the extent of the
wildland-urban interface (Radeloff et al., , 2018), which increases the
likelihood of ignition events and elevates the magnitude of damages that could
result from a wildfire. These increasing stressors, driven by climate change,
management policies and development, have combined to increase wildfire risk
(Marlon et al., , 2012) and raise the spectre of the collapse of these
ecosystems and their transition to alternative states (Adams, , 2013). Given
inherent stochasticity in the ignition and spread of wildfires, forest
ecosystems at the cusp of tipping points may exhibit flickering dynamics.

In response to these entangled and increasing risks, the USDA Forest Service
has developed a wildfire crisis strategy which centers on prescribed fire and
other fuels treatments (US Forest Service, , 2022). However, current climatic
conditions, high fuel loads, and the vast extent of the wildland-urban
interface will make the transition towards fire-resilient landscapes
challenging.

Our model suggests that a transition to a fire-resilient landscape that
exhibits flickering could strain people’s wellbeing, especially when adaptive
capacity of management agencies, communities and individuals is low. This
results from the time it takes for social practices to align with
environmental states. Further, a flickering transition to a fire-resilient
landscape may contain periods of time that resemble the historical ecosystem
state, where the pressure to invest in adaptations to increase fire resilience
may seem unnecessary. Given flickering, these intercalary chapters of
unpredictable duration which resemble historical conditions may end with
dramatic shifts marked by wildfire. The Forest Service wildfire crisis
strategy aims to minimize the risk of these conflagrations by pairing
prescribed fire with mechanical fuels treatments so that fire intensity stays
low and wildfire risk is diminished. This approach may decrease the risk of a
flickering transition, but our modeling results suggest that a complimentary
management focus on increasing people’s adaptive capacity may blunt the
negative impacts that a flickering transition can cause. For example,
investments in programs which provide education about the risks of wildfire
smoke (Wen and Burke, , 2022; Burke et al., , 2022) and resources for
improving indoor air quality could help communities navigate the transition to
a fire resilient landscape with a decreased health burden from the impacts of
air pollution.

## Discussion

Global change is pushing socio-environmental systems to the brink of tipping
points where further small changes to underlying conditions could lead to
dramatic shifts in system states. Much work has focused on identifying early
warning signals of tipping points and governance interventions that can
prevent the collapse of the current socio-environmental state. However, as
climate change and other anthropogenic impacts continue (e.g., converting
Amazonian rainforests for agricultural and cattle use), we may see more
social-ecological systems approach and pass through tipping points despite
societies’ best efforts to avoid them. In this case, in addition to efforts to
avert a tipping point, it may be valuable to design governance interventions
that minimize loss of wellbeing that results from passing through a tipping
point.

Flickering, where a system switches among alternative stable states as a
result of noise, can occur prior to a tipping point and serves as an early
warning indicator (Scheffer et al., , 2009). However, the highly unpredictable
environmental dynamics that occur under flickering pose unique challenges for
people’s environmental adaptation. In socio-environmental systems, rather than
being an early warning signal, flickering may be a primary hurdle to
successfully navigating a tipping point. Policies that bolster people’s
adaptive capacity may be vital to ensuring means are available to adapt (and
thrive) in coming decades. Tools such as (parametric) insurance (Santos et
al., , 2021), climate clubs (Nordhaus, , 2021) and risk pools (Watson et al.,
, 2018; Tilman et al., , 2018) are examples of mechanisms that could help
people maintain adaptive capacity in the face of global change.

A concerning possibility is that when people with low adaptive capacity are
exposed to environmental flickering, the reduction in their wellbeing might
further erode their adaptive capacity, for example by reducing their wealth or
health. This could result in a vicious cycle wherein flickering induces a
continuous reduction in the set of adaptation options open to people. Such a
cycle could induce conditions that force people into adopting outside options,
including urban or international migration. Human migration driven by adverse
environmental conditions is a well known consequence of climate change
(Cattaneo et al., , 2020). A term used in the context of sea-level rise to
describe the inevitability of community reorganization in the face of
environmental change is “managed retreat” (Alexander et al., , 2012). The
challenge then is to ensure that this retreat is indeed managed in a way that
accounts for the unique impacts that flickering could have during a transition
through a regime shift. For many people around the world some form of retreat
might be inevitable, either in space (i.e., human migration) or in terms of
job sector.

Income diversification is among the primary tools that people use for dealing
with environmental risks (Brouwer et al., , 2007; Shah et al., , 2021).
Therefore, flickering could incentivize people to diversify or change the
industries that secure their income. This is evident in fisheries, where
working in numerous fisheries throughout the year can act as a natural means
of buffering the stochasticity associated with harvest (Kasperski and Holland,
, 2013; Finkbeiner, , 2015; Cline et al., , 2017). Fishers are also known to
work in multiple sectors, including jobs on land within the timber and
agricultural sectors (Anderson et al., , 2017). Environmental flickering in
the oceans could lead to fishers redistributing their efforts over the set of
fisheries available to them, and other industries that they work in.
Analogously to migration, extreme environmental flickering might induce people
to permanently leave one industry for another.

While we have studied the impact passing through a single regime shift on
people’s wellbeing, in reality there may be multiple cascading environmental
tipping points (Rocha et al., , 2018). In this case of systemic environmental
risk, the associated social and environmental flickering could co-occur across
various dimensions of a person’s income portfolio. This might mean fishers
will be unable to adapt by moving sectors, and nomadic herders may no longer
find community support structures to help them recover from devastating
losses. These impacts could scale-up and result in global systemic risks
(Centeno et al., , 2015) due to the connected nature of our environment, our
socio-technological, and our governance systems. Correlated risks among marine
heatwaves at sea, droughts on land and economic volatility could interact to
present large-scale challenges for communities.

Early-warning signals of environmental regime shifts may help people manage
their adaptation to impending change (Lenton, , 2011). However, we find that
some early-warning signals of environmental regime shifts describe socio-
environmental dynamics that can already have negative impacts on people. In
these cases, our results suggest three types of governance interventions may
be warranted either individually, or in combination. First, investments in
assuring that people have high adaptive capacity can mitigate the impacts of
flickering on wellbeing by helping people remain well adapted to the rapidly
changing environment. Second, facilitating transformational change, which
partially decouples environmental adaptation and wellbeing, can result in
greater wellbeing across a broad range of conditions. Lastly, when a wellbeing
trough is unavoidable, interventions that facilitate people’s transitions to
different ways of life or migration to different places may be warranted.
Critically, these investments appear to be most benefical if enacted well
before a tipping point is crossed. This suggests that climate adaptation
policies may need to be more anticipatory than originally thought.

## Acknowledgements

The findings and conclusions in this publication are those of the authors and
should not be construed to represent any official USDA or U.S. Government
determination or policy. Simulation code is available at
https://github.com/atilman/regimeshift_wellbeing.

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Supplementary Information for:

## Maintaining human wellbeing as socio-environmental
systems undergo regime shifts

Figure SI 1: Development of livestock numbers 1966-2020. Restrictions on herd sizes and total head count were lifted after the transition from the socialist to the capitalist regime in 1990. The significant growth in livestock numbers starting in the mid-1990’s is followed by strong dynamics resulting from large livestock losses and recoveries due to climatic extremes and changing adaptive strategies. The growth in livestock numbers coincides with a drop in NDVI and rising potential evapotranspiration rates recorded by (Enebish et al., , 2020). Data publicly available from Mongolian Statistical Information System,  (2021). Figure SI 2: Development of monetary income in Mongolian Tugrik. The drop in the mid-1990’s reflects the impacts of the 1997 Asian financial crisis. A gap emerges between urban and rural incomes from the late 2000’s. Data publicly available from Mongolian Statistical Information System,  (2021). Figure SI 3: Income/expenditure ratio for rural and urban households 1966-2020. Urban household income/expenditure ratios remain relatively steady until the 1997 and continue to fluctuate strongly until 2010, when they become more steady again. This could indicate the transition to an urban regime, as urbanization started to increase significantly in the late 1990’s. Rural income/expenditure ratios are more dynamic from year-to-year, and while they have followed urban dynamics more closely since 2010, there has been a large gap between urban and rural, with rural income/expenditure ratios being significantly lower that urban ratios. This indicates the costly toll on average utility resulting from the high costs of adaptation to the flickering regime for rural households. Data publicly available from Mongolian Statistical Information System,  (2021). Figure SI 4: Population development in Mongolia 1989-2021. Data publicly available from Mongolian Statistical Information System,  (2021). Figure SI 5: Fraction of population living in urban and rural areas in Mongolia 1989-2021. Data publicly available from Mongolian Statistical Information System,  (2021). Parameter | Fig. 2 | Fig. 3 | Fig. 4 | Fig. 5 | Fig. 6 | Description
---|---|---|---|---|---|---
$l$ | n/a | n/a | 0.01 | 0.001; 0.01; 0.1 | .001 | Adaptation rate
$r$ | 1 | n/a | 1 | 1 | 1 | Resource growth rate
$K$ | 10 | n/a | 10 | 10 | 10 | Carrying capacity
$h$ | 1 | n/a | 1 | 1 | 1 | Extraction half-saturation constant
$\mu$ | n/a | n/a | 0 | 0 | 0 | Mean noise
$\beta$ | n/a | n/a | 0.07 | 0.07 | 0.07 | Noise standard deviation
$T$ | n/a | n/a | 30 | 30 | 30 | Timescale of noise decorrelation
$a$ | n/a | 3; 5 | 3 | 3 | 3; 5 | Misadaptaiton at which utility is halved
$m$ | n/a | 5; 5.75 | 5 | 5 | 5; 5.75 | Payoff function intercept
$n$ | n/a | 1/2; 1/10 | 1/2 | 1/2 | 1/2; 1/10 | Payoff function slope
$c$ | 1-3.5 | n/a | 1; 1.95; 2.45; 3.1 | 0.25-3.5 | 0.25-3.5 | Resource extraction rate
Table SI 1: Parameter values used to generate each main text figure.