 Access to this fulltext is provided by Springer Nature.
 Learn more
Download available
Content available from Scientific Reports
This content is subject to copyright. Terms and conditions apply.
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports
Localised labyrinthine patterns
in ecosystems
M. G. Clerc1, S. Echeverría‑Alar 1* & M. Tlidi2
Self‑organisation is a ubiquitous phenomenon in ecosystems. These systems can experience
transitions from a uniform cover towards the formation of vegetation patterns as a result of
symmetry‑breaking instability. They can be either periodic or localised in space. Localised vegetation
patterns consist of more or less circular spots or patches that can be either isolated or randomly
distributed in space. We report on a striking patterning phenomenon consisting of localised vegetation
labyrinths. This intriguing pattern is visible in satellite photographs taken in many territories of Africa
and Australia. They consist of labyrinths which is spatially irregular pattern surrounded by either a
homogeneous cover or a bare soil. The phenomenon is not specic to particular plants or soils. They
are observed on strictly homogenous environmental conditions on at landscapes, but they are also
visible on hills. The spatial size of localized labyrinth ranges typically from a few hundred meters to
ten kilometres. A simple modelling approach based on the interplay between short‑range and long‑
range interactions governing plant communities or on the water dynamics explains the observations
reported here.
e appearance of order and structures that involve nonequilibrium exchanges of energy and/or matter have been
widely observed in many natural systems including uid mechanics, optics, biology, ecology, and medicine1–6.
Vegetation populations and vegetation patterns belong to this eld of research. Being oen undetectable at the
soil level, largescale vegetation patterns have been rst observed thanks to the usability of aerial photographs
in the early ies7. ey appear as extended bands of vegetation alternating periodically with vegetated areas
and unvegetated bands. ese largescale botanical organisations have been reported in many semiarid and
arid ecosystems of Africa, Australia, America, and Asia. It is now widely admitted that the origin of these large
scale botanical organisations is attributed to a nonequilibrium symmetrybreaking instability leading to the
establishment of stable periodic spatial patterns. Extended and periodic vegetation pattern arising in semiarid
and arid ecosystems has been the subject of numerous studies and is by now fairly wellunderstood issue (see
recent overviews8–10 and references therein).
Vegetation patterns are not always periodic and extended in space. ey can be spatially localised and ape
riodic consisting of isolated or randomly distributed patches on bare soil11–13 or gaps embedded in a uniform
vegetation cover14,15. ey are generated in a regime where the homogeneous cover coexists with periodic vegeta
tion patterns. e interaction between wellseparated patches is always repulsive16,17 while for gaps the interaction
alternates between attractive and repulsive depending on the distance separating gaps14,17. e localised patches
has a more or less circular shape. However, for a moderate aridity condition, the circular shape can exhibit defor
mation followed by splitting of a single into two new patches. Newer patches will in their turn exhibit deformation
and selfreplication18–20 until the system reaches a periodic distribution of patches that occupies the whole space
available in landscapes19,20. is process leading to spotted periodic patterns can be seen as warnings of ecosystem
degradation and may lead to outcome of vegetation recovery. Besides patches selfreplication, circular spots can
exhibit deformation leading to the formation of arcs and spirals like in isotropic and uniform environmental
conditions21. e vegetation spirals are not waves since they do not rotate21.
In this work, we unveil a new type of vegetation pattern consisting of a localised labyrinth embedded either
in a homogeneous cover or surrounded by bare soil. is phenomenon is observed in Africa and Australia by
remote sensing imagery. An example of such a botanical selforganisation phenomenon is shown in Fig.1. ey
consist of either an irregular distribution of vegetation surrounded by a uniform cover (see Fig.1a, b), or by a
bare state (Fig.1c, d). We show that localised labyrinths embedded in a uniform cover can be stable even if the
environment is isotropic and their formation does not depend on the topography. However, when a localised
labyrinth is surrounded by a bare state, they can expand or shrink. We analyse this phenomenon by using three
OPEN
Departamento de Física and Millennium Institute for Research in Optics, Facultad de Ciencias Físicas y
Département de Physique, Faculté des
*email:
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
wellknown selforganisation vegetation models, which support localised labyrinths. We show that localised
labyrinths are permanent structures, and they can be observed worldwide involving a range of species and spatial
scales. We interpret this phenomenon as a spatial compromise between the extended labyrinth that occupies the
whole space available and stable homogeneous states. More precisely, the mechanism leading to their formation
is attributed to the pinningdepinning transition that takes place in a parameter space where models exhibit
bistability between extended disordered pattern and homogeneous cover.
Field observations of localised labyrinths
Localised labyrinths observed in nature are largescale selforganisation patterns. ey are satellite images from
Africa and Australia obtained by the use of Google Earth soware. e landscape of Central Cameroon (zone
of forestsavanna mosaic22), shown in Fig.1a, displays contrasted phases of bare and densely vegetated areas
with welldened scale and symmetry surrounded by more or less uniform woodland. e climate in the zone
where we observe the localised labyrinth is humid, with annual averaged precipitation of 1800 mm23. e annual
averaged of potential evapotranspiration is between 1500 and 1600 mm24. e localised labyrinth we observe
in Western Australia (see Fig.1b) consists of localised woodland embedded in the shrubland of Mulga Bush
(Acacia Aneura)25. In this zone the climate is arid, where the mean annual precipitation is 250 mm26 and the
mean annual potential evapotranspiration is between 1200 and 1300 mm27. Besides, the localised labyrinth can
be surrounded by bare zones as shown in landscapes of Southwest Niger in a brushgrass Savanna zone28 (Fig.1c,
d). In this region the climate is semiarid, the mean annual rainfall is 605 mm, in between June and September29,
and the annual mean potential evapotranspiration near this zone is 1900 mm30.All the climate data is summa
rized in Table1 in Methods section. Sparsely populated or bare areas alternate with dense vegetation irregular
bands or patches made of microchloa Indica. e eld observations suggest that localised labyrinthine structures
are formed both in a at landscape and with topographic variation (see Fig.2). By their spatial regularity, by
their spatial scales ranging from a few hundred meters to ten kilometres, as well as by the composition of their
vegetation (tree, shrubs, herbs, and grasses), localised labyrinthine patterns are permanent structures, and they
Figure1. Localised labyrinth vegetation patterns. Top views of (a) Central Cameroon (3
◦
59′ 22.05″ N 12
◦
17′ 20.99″ E), (b) Western Australia (29
◦
33′ 36.16″ S 117
◦
15′ 32.60″ E), (c) and (d) Southwest Niger (12
◦
34′
45.10″ N 2
◦
41′ 28.71″ E and 12
◦
22′ 6.72″ N 3
◦
28′ 39.35″ E, respectively). e inset (d) show a zoom of the
characteristic labyrinth pattern. All the images were retrieved from Google Earth soware (https:// earth. google.
com/ web/) with a resolution of
1920 ×1080
pixels (total areas of (a) 196.5
km2
, (b) 7.4
km2
, (c) 12.3
km2
, and
(d) 24.6
km2
).e satellite images were taken on 17 of February, 2021; 22 of September, 2018; 15 of November,
2016; and 12 of February, 2020, respectively. e upperright insets show the localised patterns to emphasize the
topography of the landscape.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
Figure2. Elevation proles of the localised labyrinths observed in nature (Fig.1). ey were obtained using Google Earth
soware (https:// earth. google. com/ web/). In each zone two elevation proles are shown for two arbirtary crosssections (L1
and L2). (a) e localised labyrinth in central Cameroon has large uctuations in height ranging from 665 to 745 m. e
homogenous cover that surrounds the localised labyrinth also has uctuations in height of the same order. e size of the
major axis of the localised pattern is 16.7 km. (b) In Western Australia the localised labyrinth is in a gentle slope (
0.8%
), t he
size of its major axis is 0.6 km. (c) and (d) shows the elevation proles of the localised labyrinths in Southwest Niger. Both
patterns are in small hills of about 10 m, surrounded by a bare state. ese localised labyrinths emerge in plain terrains.
e sizes of the major axes are 1.0 km and 2.0 km, respectively. See the "Methods" section for details on the accuracy of the
elevation data.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
can be observed even in nonarid climates. ey have neither been observed nor reported. Understanding their
formation and maintenance is an important ecological issue.
e mechanism underlying the emergence of the localised labyrinth can be captured by using selforganisa
tion mathematical models that can explain vegetation pattern formation within a unied conceptual framework.
In this respect, two approaches will be used. e rst is based on the relationship between the plants’ aerial
subterranean structures, the facilitative and competitive feedbacks which act at the community level, and the
plants’ spatial propagation by seed dispersion31,32. e second approach incorporates explicitly water transport
by below ground diusion and/or above ground runo33–35. ese models are in reasonable agreement with
the eld observations36–38.
Mathematical modelling of ecosystems
e absence of the rst principles for biological systems in general, and in particular for vegetation populations
where phenomena are interconnected makes their mathematical modelling complex. e theory of vegetation
pattern formation rests on the selforganisation hypothesis and symmetrybreaking instability that provoke the
fragmentation of the uniform cover. e symmetrybreaking instability takes place even if the environment is
isotropic31,33,35. is instability may be an advectioninduced transition that requires the preexistence of the
environment anisotropy due to the topography of the landscape34,39,40. Generally speaking, this transition requires
at least two feedback mechanisms having a shortrange activation and a longrange inhibition. In this respect,
we consider three dierent vegetation models that are experimentally relevant systems: (i) the generic interac
tion redistribution model describing vegetation pattern formation which incorporates explicitly the facilitation,
competition and seed dispersion nonlocal interactions (ii) the local nonvariational partial dierential model
described by a nonvariational Swi–Hohenberg type of model equation, and (iii) the reaction–diusion system
that incorporate explicetely water transport.
The interaction‑redistribution approach. e integrodierential model. is approach consists of
considering a wellknown logistic equation with nonlocal planttoplant interactions. ree types of interactions
are considered: the facilitative
M
f
(r,t)
, the competitive
Mc(r,t)
, and the seed dispersion
Md(r,t)
nonlocal inter
actions. To simplify further the mathematical modelling, we consider that the seed dispersion obeys a diusive
process
Md(
r
,t)≈∇
2b(
r
,t)
, with D the diusion coecient, b the biomass density, and
∇2
=∂
2
/∂x
2
+∂
2
/∂y
2
is the Laplace operator acting in the (x,y) plane. e interactionredistribution reads
where
i
=
f,c
.
ξi
represents the strength of the interaction,
Ni
is a normalisation constant. We assume that their
Kernels
φi(r,t)
are exponential functions with
Li
the range of their interactions. e facilitative interaction
Mf(r,t)
favouring vegetation development. ey involve the accumulation of nutrients in the neighbourhood
of plants, the reciprocal sheltering of neighbouring plants against climatic harshness which improves the water
budget in the soil. e range of the facilitative interaction
Lf
operates on the crown size. e competitive interac
tion operates over a length
Lc
and involves the belowground structures, i.e., the rhizosphere. In nutrientpoor
or/and in waterlimited territories, lateral spreading may extend beyond the radius of the crown. is extension
of roots relative to their crown size is necessary for the survival and the development of the plant in order to
extract enough nutrients and/or water from the soil. When incorporating these nonlocal interactions in the
paradigmatic logistic equation, the spatiotemporal evolution of the normalised biomass density
b(r,t)
in isotropic
environmental conditions reads14
e normalisation is performed with respect to the total amount of biomass supported by the system. e
rst two terms in the logistic equation with nonlocal interaction Eq.(2) describe the biomass gains and losses,
respectively. e third term models seed dispersion. e aridity parameter
µ
accounts for the biomass loss and
gain ratio, which depends on water availability and nutrients soil distribution, topography, etc. e homogene
ous cover solutions of Eq.(2) are:
bo=0
which corresponds to the state totally devoid of vegetation, and the
homogeneous cover solutions satisfy the equation
with
�=ξf−ξc
measures the community cooperativity if
�>0
or anticooperativity when
�<0
. e bare
state
bo=0
is unstable (stable)
µ<1
(µ > 1
). e homogeneous cover state with higher biomass density is stable
and the other is unstable. ese solutions are connected by a saddlenode or a tipping point whose coordinates
are given by
b
sn
=(� −1)/�,µ
sn
=e
�
−
1
/�
. e linear stability analysis of vegetated cover (
bs
) with respect
to small uctuations of the from
b(r,t)
=
bs
+
δb exp
{
σt
+
ik
·
r}
with
δb
small, yields the dispersion relation
Given the spatial isotropy, the growth rate
σ(k)
is a real quantity. is eigenvalue may become positive for a nite
band of unstable modes which triggered the spontaneous amplication of spatial uctuations towards the forma
tion of periodic structures with a welldened wavelength. At the symmetrybreaking instability the value of the
critical wavenumber
kc
marking the appearance of a band of unstable modes, and hence the symmetrybreaking
(1)
M
i=exp
ξi
Ni
b(r+r′,t)φi(r,t)dr′
, with φi(r,t)=exp(−r/Li
)
(2)
∂t
b(r,t)=b(r,t)[1−b(r,t)]M
f
(r,t)−µb(r,t)M
c
(r,t)+D∇
2
b(r,t)
.
(3)
µ
=
(1
−
b)exp(�b),
(4)
σ(
k)=
bs(1−bs)ξf−bs−
b
s
(1−b
s
)ξ
c
(1+
L
2
ck
2)3/2
eξfbs−Dk2
.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
instability, can be evaluated by two conditions:
σ(kc)=0
and
∂σ /∂ kkc=0
. ese conditions yield the most
unstable mode
is critical wavenumber determines the wavelength of the periodic vegetation pattern
2π/kc
that emerges from
the symmetrybreaking instability. Replacing
kc
in the condition
σ(kc)=0
, we can then calculate the critical
biomass density
bc
. e corresponding critical aridity parameter
µc
is provided explicitly by the homogeneous
steady states Eq.(3).
Local model: a nonvariational Swi–Hohenberg model. e integrodierential equation(2) can be reduced
by means of a multiplescale analysis to a simple partial dierential equation, in the form of nonvariational
Swi–Hohenberg equation. is reduction has been performed in the neighbourhood of the critical point asso
ciated with the nascent bistability14,32. e coordinates of the critical point are: the biomass density
bc=0
, the
cooperativity parameter
c=1
, and the aridity parameter
µc=1
. ese coordinates are obtained from Eq.(3)
by satisfying the double condition
∂µ/∂ bs=0
and
∂2
µ/∂
b2
s
=
0
. To apply a multiplescale analysis it is neces
sary to dene a small parameter that measures the distance from criticality and expand b,
µ
, and
in the Taylor
series around their critical values. e symmetrybreaking instability should be close to that critical point. To
full this condition, we must consider a small diusion coecient in order to include the symmetrybreaking
instability in the description of the dynamics of the biomass density. is reduction is valid in the double limit of
nascent bistability and close to the symmetrybreaking instability. In this double limit, the timespace evolution
of biomass density obeys a nonvariational Swi–Hohenberg model14
where
η
and
κ
are, respectively, the deviations of the aridity and cooperativity parameters from their values at
the critical point. e linear and nonlinear diusion coecients
ν
,
γ
, and
α
depend on the shape of kernels17. In
addition to the bare state
u=0
, the homogeneous covers obey
where the two homogeneous solutions
u±
are connected through the saddlenode bifurcation
u
sn
=κ/2, η
sn
=κ
2
/4
, with
κ>0
. e solution
u−
is always unstable even in the presence of small spatial
uctuations. e linear stability analysis of vegetated cover (
u+
) with respect to small spatial uctuations, yields
the dispersion relation
Imposing
∂σ /∂ k

kc
=
0
and
σ(kc)=0
, the critical mode can be determined
where
uc
satises
4
α
u2
c
(
2uc
−κ) =(
2
γ
uc
−ν)
2
.e corresponding aridity parameter
ηc
can be calculated from
Eq.(7).
The reaction–diusion approach. e second approach explicitly adds the water transport by below
ground diusion. e coupling between the water dynamics and the plant biomass involves positive feedbacks
that tend to enhance water availability. Negative feedbacks allow for an increase in water consumption caused by
vegetation growth, which inhibits further biomass growth.
e modelling considers the coupled evolution of biomass density
b(r,t)
and groundwater density
w(r,t)
. In
its dimensionless form, this model reads33
e rst term in the rst equation describes plant growth at a constant rate (
γ /ω
) that grows linearly with w
for dry soil. e quadratic nonlinearity
−b2
accounts for saturation imposed by poor nutrients soil. e term
proportional to
θ
accounts for mortality, grazing or herbivores. e mechanisms of dispersion are modelled by
a simple diusion process. e groundwater evolves due to a precipitation input p. e term
(1−ρb)w
in the
second equation accounts for the evaporation and drainage, that decreases with the presence of vegetation. e
term
w2b
models the water uptake by the plants due to the transpiration process. e groundwater movement
follows the Darcy’s law in unsaturated conditions; that is, the water ux is proportional to the gradient of the
water matric potential41. e matric potential is equal to w, under the assumption that the hydraulic diusivity
(5)
k
2
c=
1
L2
c
3bseξfbs(1−bs)ξcL2
c
2D2/5
−1
.
(6)
∂tu(
r
,t)
=−
u(
r
,t)(η
−
κu(
r
,t)
+
u(
r
,t)2)
+
[ν
−
γu(
r
,t)]
∇
2u(
r
,t)
−
αu(
r
,t)
∇
4u(
r
,t),
(7)
u
±=κ±
κ2−4η
2,
(8)
σ(k)
=
u+(κ
−
2u+)
−
(ν
−
γu+)k2
−
αu+k4.
(9)
k
c=
γ−ν/uc
2α,
(10)
∂b
∂t
=γ
w
1+ωw
b−b2−θb+∇
2b
,
(11)
∂w
∂t
=p−(1−ρb)w−w2b+δ∇2(w−βb)
.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
is constant41. To model the suction of water by the roots, a correction to the matric potential is included;
−βb
,
where
β
is the strength of the suction.
Results
Localised labyrinthine vegetation pattern. In our analysis, we focus on the simplest vegetation model
that has been derived from theinteractionredistribution approach, namely the nonvariational Swi–Hohen
berg Eq.(6) described above. is model is appropriate to describe the spacetime dynamics of the biomass
under resourcelimited landscapes such as nutrient limitation or water deprivation. In this case, the average bio
mass density is low comparing the carrying capacity closedpacking density of unstressed vegetation. e simu
lated stationary localised vegetation labyrinth is shown in Fig.3a. Moreover, to conrm the eld observation
and to show that this phenomenon is modelindependent, we conducted numerical simulations of the other two
models, the integrodierential (Eq.(2)) based on the facilitative, competitive, and seed dispersion interactions;
and the reaction–diusion type that explicitly incorporates water transport (Eq.(11)). e results are shown in
Fig.3b and 3c. e parameters used to simulate the dierent localised labyrinths are listed in Tables2,3, and4 in
the "Methods" section. e localised labyrinth consists of one spatially disordered state surrounded by a quali
tatively dierent state. Note that the localised labyrinthine patterns shown in Fig.3 do not have a round shape.
e fact that this shape is not round is attributed to the presence of defects in the disordered pattern since they
modify the interface energy. Investigations of fronts propagation between labyrinths and homogeneous states
mediated by defects are missing in the literature. e interface separating these two states is stationary leading to
a xed size of a localised labyrinth. It neither grows and invades the uniform cover nor shrinks. e stabilization
of localised labyrinth is attributed to the interface pinning phenomenon42,43. is phenomenon is characterized
by an interface that connects a homogeneous state and a periodic one, which is motionless on a nite region of
parameters, pinning range. is pinning eect occurs due to the competition between a global energy symmetry
breaking between states that favors the interface propagate in one direction and the spatial modulations that
block the interface by introducing potential barriers42.
To determine the stability domain of the localised labyrinth, we establish the bifurcation diagram shown in
Fig.4a, where we plot the biomass density as a function of the aridity parameter
η
. e aridity refers not only
to water scarcity but can be also attributed to the nutrientpoor soil. When the aridity is low obviously the uni
form vegetated state is stable (blue line) and the bare state (broken line) is unstable. When the aridity parameter
is further increased, the homogeneous cover becomes unstable with respect to small uctuations. Above this
symmetrybreaking instability, several branches of solutions emerge subcritically for
η<η
c
. Example of veg
etation patterns that appears follows the wellknown sequence made sparse vegetation spots that can be either
periodic or localised in space (see i, Fig.4a), banded vegetation (see ii, Fig.4a) or a periodic distribution of
localised patches setting on the bare state (see iii, Fig.4a).
An extended labyrinthine pattern can be generated subcritically as indicated by the red line in the bifurcation
diagram (see Fig.4a). e situation which interests us requires that this extended labyrinth exhibits a coexistence
with the uniform vegetated state. e coexistence between these two qualitatively dierent states is the prereq
uisite condition for the formation of a stable localised labyrinth. However, this condition is necessary but not
sucient, the interface separating these two states exhibits a pinning phenomenon42. Indeed, as shown in the
inset of Fig.4a, there exists a nite range of the aridity parameter oen called the pinning zone
η−
p
<η<η
+
p
,
where localised labyrinthine patterns are stable. Examples of localised labyrinth obtained by numerical simula
tions for xed values of the control parameters are shown in Fig.4a (iv, v, vi). e motionless interface is not
necessarily circular, and contains bands perpendicular to it and circular patches. Similar bifurcation diagram is
obtained from the integrodierential model (see Fig.4b).
Finally, we discuss the situation where the aridity is not homogenous due to the topography. For this purpose,
we choose a top hatlike shape for the aridity parameter as shown in Fig.5a. In this case, numerical simulations
of the integrodierential model Eq.(2) show a stable localised labyrinthine pattern (see Fig.5a). Note that the
localised labyrinthine structures surrounded by bare soil shown in Fig.1c, d are unstable since the interface
propagates. e interface can not be pinned in the absence of spatial oscillations around the bare state. Oscilla
tions around this state are unphysical since the biomass density is a positive dened quantity. However, when
the aridity parameter possesses an inverted top hatlike shape, it is possible to pin the interface (see Fig.5b). In
this case, the localised labyrinthine pattern is surrounded by a mosaic extended state, and the mechanismof
stabilization is rather due to the inhomogeneity of the aridity parameter.
Deppining mechanism. e spatial location of the localised labyrinth immersed in the bulk of the stable
uniform vegetated state depends on the initial condition considered. When ecosystems operate out of the pin
Table 1. Mean annual precipitation, potential evapotranspiration, and aridity index of the regions where
localised labyrinthine patterns are observed. For more details on the meteorological data see the references
given in the text.
Precipitation (mm) Potential evapotranspiration (mm) Aridity index Classication
Central Cameroon 1800 1500–1600 1.1–1.2 Humid
Western Australia 250 1200–1300 0.1–0.2 Arid
Southwest Niger 605 1900 0.3 Semiarid
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
ning zone, the interface separating the labyrinth and the homogeneous cover propagates due to the depinning
transition (see Fig.6a, b). In this case, depending on the aridity level, the interface propagates from one stable
state to another e transition is dierent when moving the aridity parameter slowly or abruptly. In the second
type of variation, when
η<η
−
p
, the homogeneous cover invades the system, while when
η>η
+
p
, the localised
labyrinth survives but it is now embedded by a periodic distribution of gaps (see Fig.6b).
Conclusions
In this paper we have reported for the rst time evidence of localised labyrinthine vegetation patterns observed
on satellite images from Africa and Australia. We have shown that these localised structures are robustly con
sisting of either an irregular distribution of vegetation surrounded by a uniform cover or on the contrary sur
rounded by a bare state. We have shown that the formation of localized labyrinthine patterns is not specic to
particular plants or soils. We have found localised labyrinths in ecosystems on at landscapes and hills. ree
relevant models which undergo localised vegetation labyrinthine patterns have been considered; (i) vegeta
tion interactionredistribution model of vegetation dynamics, which can generate patterns even under strictly
homogeneous and isotropic environmental conditions. It is grounded on a spatially explicit formulation of the
balance between facilitation and competition. Ecosystems experience transitions towards landscape fragmenta
tion of landscapes (ii) the nonvariational Swi–Hohenberg model that can be derived from the model (i) in the
longwavelength pattern forming regime, and (iii) reaction–diusion model that incorporates explicitly water
transport. We have shown that all these models despite their mathematical structure support the phenomenon
Figure3. Numerical observations of localised labyrinths. e modelindependent structure is observed in (a)
a nonvariational Swi–Hohenberg model, (b) integrodierential nonlocal model, and (c) reaction–diusion
model. In the three cases the labyrinth is supported by a uniform vegetated state. e parameters used in each
model are listed in the "Methods" section. From numerical simulations, the gure was created using Inkscape
1.0 (https:// inksc ape. org/ relea se/ inksc ape1. 0/).
Table 2. List of parameter values of the simulations of the nonvariational Swi–Hohenberg equation, shown
in Fig.3a (
200 ×200
grid,
η
=
1.01
), Fig.4a (
120 ×120
grid, [i, ii, iii] with space step
x=0.5
), and Fig.6
(
120 ×120
grid).
Cooperativity (
κ
)
ν
γ
α
Time step (
t
) Space step (
x
)
0.6 0.011 0.5 0.125 0.05 0.8
Table 3. List of parameter values of the simulations of the integrodierential model, shown in Fig.3b
(
512 ×512
grid,
µ
=
1.301
), Fig.4b (
256 ×256
grid), and Fig.5 (
256 ×256
grid).
Competition length (
Lc
) Diusion (D) Facilitation strength (
ξf
)Competition strength (
ξc
) Time step (
t
) Space step (
x
)
2.5 1 3 1 0.1 0.8
Table 4. List of parameter values of the simulation of the reaction–diusion model, shown in Fig.3c
(
512
×
512
grid).
γ
ω
θ
p
ρ
δ
β
Time step (
t
) Space step (
x
)
1.45 1.5 0.2 0.7 1.5 100 2.7 0.001 0.6
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
of the localised labyrinth. We have established their bifurcation diagram and identied a parameter region,
where we have observed a coexistence between a homogeneous cover and an extended labyrinthine structure
which are both linearly stable. Within it, there exist a pinning zone of parameters where localised labyrinthine
vegetation patterns have been generated as a stable pattern. Note however that localised labyrinth is determined
by the initial condition, while their maximum peak biomass remains constant for a xed value of the system
parameters. is phenomenon results from front pinning between qualitatively dierent coexisting vegetation
states. Outside of the pinning region, we have shown that the localised labyrinth either shrink and leads to the
formation of regular distribution of circular spots or expand leading to the formation of an extended labyrinth.
Finally, we have investigated the formation of localised labyrinth on a hill by considering an inhomogenous
aridity parameter. is forcing acts as a trapping potential for the labyrinthine pattern. Owing to its general
character, robust localised labyrinthine structures observed and predicted in our analysis should be observed in
other systems of various elds of natural science such as uid mechanics, optics, and medicine.
We have documented for the rst time the phenomenon of localised vegetation labyrinth by remote observa
tions, using the Google Earth computer program, and numerical simulations of three dierent theoretical models
Figure4. Bifurcation diagram of vegetation models. (a) the nonvariational Swi–Hohenberg model, and (b)
the integrodierential model. Gaps (i), labyrinths (ii), and spots (iii) is the standard sequence of patterns in
vegetation models.
u
and
b
stands for the average biomass in each model. e solid curves indicate where the
bare soil or uniform vegetation cover are stable, whereas the segmented curves indicate where these states are
unstable. In (a), the critical point
(ηc=0.038, uc=0.53)
stands for the instability threshold where the uniform
vegetated cover loses stability to a modulated state. In a narrow region, between
η
−
p=0.010
and
η+
p
=
0.013
,
where there is a multistability of states (labyrinth, uniform vegetation, bare soil) the emergence of localized
labyrinths is possible. In (b),
(µc=1.309, bc=0.62)
and
µ
−
p=1.2950
,
µ+
p
=
1.3044
. e insets with the
pinning zones enlarged show the existence of a family of localized labyrinths (triangles) with dierent average
biomasses. e insets (iv), (v), and (vi) show dierent localized labyrinthine patterns [(a) and (b)]. e other
parameters are provided in the "Methods" section. From numerical simulations, the gure was created using
Inkscape 1.0 (https:// inksc ape. org/ relea se/ inksc ape1. 0/).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
which are based on ecologically realistic assumptions. ese models provide a clear explanation of how nonlinear
plantplant interactions and the eects of plants on soil water can be crucial in determining the spatial distribu
tion of plant communities. It is far from the scope of this contribution to provide parameters assessment and
comparison between the theoretical predictions and the eld observations. Work in this direction is in progress.
Extended and localised vegetation labyrinthine patterns opens a whole new area of research in selforgani
sation in vegetation pattern formation, where eld observations will be fundamental to establish a connection
with the concepts developed in this work.
Methods
Google Earth data. e satellite images (cf. Fig.1) are retrieved from the openaccess program Google
Earth (see the link https:// earth. google. com/ web/ and information there), courtesy of CNES/Airbus, Landsat/
Copernicus, and Maxar Technologies (Fig.1a), and CNES/Airbus (see Fig.1b–d).
e elevation proles in Fig.2 are obtained from Google Earth. is soware uses digital elevation data from
the Shuttle Radar Topography Mission at a resolution of 30 m44,45. e error, at a
90%
condence level, associated
to the absolute height data is less than 6 m for the territories considered here (Africa and Australia)44.
Climate data. Localised labyrinthine patterns are observed in Central Cameroon (Fig.1a), Western Aus
tralia (Fig.1b), and Southwest Niger (Fig.1c, d). e climate types of these regions are humid, arid, and semi
arid, respectively. e climatic classication is based on the aridity index (see Table1), which is the ratio of mean
annual precipitation and potential evapotranspiration46. Note that the aridity index is small (big) when the arid
ity parameter (
η
or
µ
), dened in the interactionredistribution approach subsection, is big (small).
Numerical simulations data. Numerical simulations of models under consideration were solved in
square grids with RungeKuttta 4 time integrator. e spatial derivatives were approximated using nite dier
ence scheme with a three point stencil using periodic boundary conditions. In the integrodierential simulation,
the convolution integrals were solved in Fourier space through DFT algorithms. e detail of the parameters
used in the numerical simulations are listed in the Tables below.
Generation of numerical localised labyrinthine patterns. e localised labyrinthine patterns are
initialised in a regionof parameters where the uniform vegetation cover and the labyrinthine pattern coexist,
in particular, in a pinning zone (see Fig.4). e initial condition consists of a circular patch of labyrinthine pat
tern in the centre of the simulation box, embedded in a homogenous background (see Fig.7). Aer a transient
accommodation of the biomass eld, the stable localised labyrinth emerges. e dynamics towards equilibrium
in the integrodierential model Eq.(2) is resumed in Fig.7 and the Supplementary Video S1.
Computation of the bifurcation diagrams. e bifurcation diagrams in Fig.4 were determined with
analytical and direct numerical integration techniques of the governing equations. e blue and black curves
account for the vegetated state and the bare one, respectively. e curves are solid when the corresponding state
is stable, and broken if unstable. e critical points in which the dierent states change their stability are deter
mined by linear analysis, detailed in the interactionredistribution approach subsection.
e red curve is the stable branch of labyrinthine patterns, and it is determined by direct numerical integra
tion of the governing equations (using the algorithm explained above). Starting from a vegetated state with a
small amplitude noise perturbation, in the region where the uniform vegetation state is unstable, a stable laby
rinthine pattern can emerge (see (ii) in Fig.4). e stability range of the labyrinth state, that is (i) and (iii) in
Figure5. Localised labyrinthine patterns generated by inhomogenous aridity in the integrodierential model.
e spatially forced pattern can be supported by (a) the vegetated state (top hatlike shape
µ
parameter), and
(b) the bare state (inverted top hatlike shape
µ
parameter). From numerical simulations, the gure was created
using Inkscape 1.0 (https:// inksc ape. org/ relea se/ inksc ape1. 0/).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
Fig.4, are found by decreasing/increasing the aridity parameter starting from the labyrinthine pattern (see the
black arrows in Fig.4).
e blue triangles account for the stable branch of the localised labyrinthine pattern. e initial condition is
a stable localised labyrinth state (cf. state (iv) in Fig.4). e aridity is decreased until the localised labyrinthine
pattern becomes a localised hexagonal pattern, which determines the le boundary of the pinning region (
η
−
p
or
µ
−
p
). On the other hand, the right boundary of the pinning region (
η+
p
or
µ+
p
) is determined by increasing the
aridity until the localised labyrinthine pattern invades all the system (see Fig.6).
Figure6. Deppining transitions of a localised labyrinth state (
η=0.0102
) in a nonvariational Swi–
Hohenberg model. is state is shown in the middle panel of (a) and (b). e localised pattern destabilize when
crossing the pinning region boundaries when varying slowly (a) or abruptly (b) the aridity parameter. In the
rst case (a), when decreasing
η
the localized labyrinth loses its internal structure due to shrinking of stripes (le
panel,
η=0.007
), and when increasing
η
some stripes begin to grow at the interface of the localized labyrinth
and a hexagon pattern starts to invade the uniform cover (right panel,
η=0.016
) . In the second case (b), when
decreasing
η
all the stripes and patches of sparse vegetation disappear in favor of a uniform vegetated cover
(le panel,
η=−0.03
), and when increasing
η
the vegetated cover becomes unstable and stripes emerge. is
process transform the localised labyrinth into an extended one (right panel,
η=0.05
). e other parameters
are provided in the "Methods" section. From numerical simulations, the gure was created using Inkscape 1.0
(https:// inksc ape. org/ relea se/ inksc ape1. 0/).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
Received: 28 April 2021; Accepted: 10 August 2021
References
1. Cross, M. C. & Hohenberg, P. C. Pattern formation outside of equilibrium. Rev. Mod. Phys. 65, 851 (1993).
2. Murray, J. D. Mathematical biology. In Biomathematics Vol. 19 (Springer, 1989).
3. Akhmediev, N. & Ankiewicz, A. (eds) Dissipative Solitons: From Optics to Biology and Medicine. Lecture Notes in Physics Vol. 751
(Springer, 2008).
4. Tlidi, M., Staliunas, K., Panajotov, K., Vladimiorv, A. G. & Clerc, M. G. Localized structures in dissipative media: From optics to
plant ecology. Philos. Trans. R. Soc. A 372, 20140101 (2014).
5. Yochelis, A., Gilad, E., Nishiura, Y., Silber, M. & Uecker, H. Special issue: Advances in pattern formation. Physica D 415, 132769
(2021).
6. Clerc, M. G., Coullet, P., Rojas, R. G. & Tlidi, M. Introduction to focus issue: Instabilities and nonequilibrium structures. Chaos
30, 110401 (2020).
7. Macfadyen, W. A. Soil and vegetation in British Somaliland. Nature 165, 121 (1950).
8. Ridol, L., D’Odorico, P. & Laio, F. NoiseInduced Phenomena in the Environmental Sciences (Cambridge University Press, 2011).
9. Meron, E. Nonlinear Physics of Ecosystems (CRC Press, Taylor & Francis Group, 2015).
10. Ké, S. & Couteron, P. Spatiotemporal patterns as indicators of approaching critical transitions. Ecol. Ind. 94, 491 (2018).
11. Lejeune, O., Tlidi, M. & Couteron, M. Localized vegetation patches: A selforganized response to resource scarcity. Phys. Rev. E
66, 010901(R) (2002).
12. Rietkerk, M., Dekker, S. C., Ruiter, P. C. & van de Koppel, J. Selforganized patchiness and catastrophic shis in ecosystems. Science
305, 1926 (2004).
13. Meron, E., Yizhaq, H. & Gilad, E. Localized structures in dryland vegetation: Forms and functions. Chaos 17, 037109 (2007).
14. Tlidi, M., Lefever, R. & Vladimirov, A. On vegetation clustering, localized bare soil spots and fairy circles. Lect. Notes Phys. 751,
381 (2008).
15. Tarnita, C. E. et al. A theoretical foundation for multiscale regular vegetation patterns. Nature 541, 398 (2017).
16. BerríosCaro, E., Clerc, M. G., Esca, D., Sandivari, C. & Tlidi, M. On the repulsive interaction between localised vegetation patches
in scarce environments. Sci. Rep. 10, 5740 (2020).
17. Tlidi, M., BerríosCaro, E., PintoRamo, D., Vladimirov, A. G. & Clerc, M. G. Interaction between vegetation patches and gaps: A
selforganized response to water scarcity. Physica D 414, 132708 (2020).
18. Meron, E., Gilad, E., von Hardenberg, J., Shachak, M. & Zarmi, Y. Vegetation patterns along a rainfall gradient. Chaos, Solitons &
Fractals 19, 367 (2004).
Figure7. Initialization and stabilization of localised labyrinthine pattern in the integrodierential model
Eq.(2). e aridity parameter is
µ=1.3
. e other parameters are summarized in Table3 on "Methods"
section. e sequence
t1=1
to
t6=4·105
accounts for the evolution towards equilibrium of the localised
labyrinthine pattern, starting from a circular patch of a labyrinth state embedded in a vegetated background
(
t1
). e curve in the right shows the evolution of the average biomass density
b
, that is the double integral of
the two dimensional biomass eld b divided by the area of the simulation box (see the Integrodierential model
subsection). e stable labyrinthine pattern is reached in
t5≈105
iterations of the RK4 time integrator, when
there is no change in
b
. From numerical simulations, the gure was created using Inkscape 1.0 (https:// inksc
ape. org/ relea se/ inksc ape1. 0/).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports  (2021) 11:18331 
www.nature.com/scientificreports/
19. Bordeu, I., Clerc, M. G., Couteron, P., Lefever, R . & Tlidi, M. Selfreplication of localized vegetation patches in scarce environments.
Sci. Rep. 6, 33703 (2016).
20. Tlidi, M., Bordeu, I., Clerc, M. G. & Esca, D. Extended patchy ecosystems may increase their total biomass through selfreplication.
Ecol. Ind. 94, 534 (2018).
21. Tlidi, M. et al. Observation and modelling of vegetation spirals and arcs in isotropic environmental conditions: Dissipative struc
tures in arid landscapes. Philos. Trans. R. Soc. A (Lond.) 376, 20180026 (2018).
22. Mermoz, S., Le Toan, T., Villard, L., RéjouMéchain, M. & SeifertGranzin, J. Biomass assessment in the Cameroon savanna using
ALOS PALSAR data. Remote Sens. Environ. 155, 109 (2014).
23. Manetsa, V. Étude multiéchelles des précipitations et du couvert végétal au Cameroun: Analyses spatiales, tendances temporelles,
facteurs climatiques et anthropiques de variabilité du NDVI (Doctoral dissertation) (Université de Bourgogne, 2011).
24. Africa Energy Unit. Understanding the Impact of Climate Change on Hydropower: e Case of Cameroon (Africa Energy Unit,
2014).
25. Beard, J. S. e vegetation survey of western Australia. Vegetation 30, 3 (1975).
26. England, M., Ummenhofer, C. & Santoso, A. Interannual rainfall extremes over southwest western Australia linked to Indian
Ocean climate variability. J. Clim. 19, 1948–1969 (1948).
27. Chiew, F., Wang, Q. J. & McConachy, F. Evapotranspiration maps for Australia. In Hydrology and Water Resources Symposium
(2002).
28. Wallace, J. S. & Holwill, C. J. Soil evaporation from tigerbush in southwest Niger. J. Hidrol. 188, 426 (1997).
29. Casse, C. et al. Potential of satellite rainfall products to predict Niger river ood events in Niamey. Atmos. Res. 163, 162 (2015).
30. Ashaolu, E. & Iroye, K. Rainfall and potential evapotranspiration patterns and their eects on climatic water balance in the Western
Lithoral hydrological zone of Nigeria. Ruhuna J. Sci. 9, 2 (2018).
31. Lefever, R. & Lejeune, O. On the origin of tiger bush. Bull. Math. Biol. 59, 263 (1997).
32. Lejeune, O. & Tlidi, M. A model for the explanation of vegetation stripes (tiger bush). J. Veg. Sci. 10, 201 (1999).
33. von Hardenberg, J., Meron, E., Shachak, M. & Zarmi, Y. Diversity of vegetation patterns and desertication. Phys. Rev. Lett. 87,
198101 (2001).
34. Klausmeier, C. A. Regular and irregular patterns in semiarid vegetation. Science 284, 1826 (1999).
35. HilleRisLambers, R., Rietkerk, M., van den Bosch, F., Prins, H. H. T. & de Kroon, H. Vegetation pattern formation in semiarid
grazing systems. Ecology 82(50), 50 (2001).
36. Barbier, N., Couteron, P., Lefever, R. & Deblauwe, V. Spatial decoupling of facilitation and competition at the origin of gapped
vegetation patterns. Ecology 89, 1521 (2008).
37. Couteron, P. et al. Plant clonal morphologies and spatial patterns as selforganized responses to resourcelimited environments.
Philos. Trans. R. Soc. A (Lond.) 372, 20140102 (2014).
38. Getzin, S. et al. Adopting a spatially explicit perspective to study the mysterious fairy circles of Namibia. Ecography 38, 1 (2015).
39. Okayasu, T. & Aizawa, Y. Systematic analysis of periodic vegetation patterns. Prog. eor. Phys. 106, 705 (2001).
40. Sherratt, J. A. An analysis of vegetation stripe formation in semiarid landscapes. J. Math. Biol. 51, 183 (2005).
41. Hillel, L. Introduction to Environmental Soil Physics (Academic Press, 2004).
42. Pomeau, Y. Front motion, metastability and subcritical bifurcations in hydrodynamics. Physica D 23, 3 (1986).
43. Haudin, F. et al. Driven front propagation in 1D spatially periodic media. Phys. Rev. Lett. 103, 128003 (2009).
44. Farr, T. G. et al. e shuttle radar topography mission. Rev. Geophys. 45, 2 (2007).
45. ElAshmawy, K. L. A. Investigation of the accuracy of google earth elevation data. Artif. Satt. 51, 3 (2016).
46. United Nations Environment Programme. (World Atlas Desertication, 1992).
Acknowledgements
MGC thanks for the nancial support of FONDECYT Project 1210353 and ANIDMillennium Science Initiative
ProgramICN17_012. S.E.A. thanks the nancial support of ANID by Beca Doctorado Nacional 202021201376.
MT received support from the Fonds National de la Recherche Scientique (Belgium). e authors gratefully
acknowledge the nancial support of WallonieBruxelles International (WBI).
Author contributions
M.G.C. and M.T. designed the research. S.E.A. conducted numerical simulations and the data analysis. All
authors worked on the theoretical description and draed the paper, and contributed to the overall scientic
interpretation and edited the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598 021 974724.
Correspondence and requests for materials should be addressed to S.E.A.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Open Access is article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons licence, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
© e Author(s) 2021
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small
scale personal, noncommercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be noncommercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commonslicensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal noncommercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for interlibrary loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.