Balbie al. En i onmen al E idence (2022) 11:5
h ps://doi.o g/10.1186/s13750-022-00258-y
COMMENTARY
The global en i onmen al agenda u gen ly
needs aseman ic web o knowledge
S e ano Balbi1,2* , Kenne h J. Bags ad3, Ainhoa Mag ach1,2, Ma ia Jose Sanz1,2, Naikoa Aguila ‑Amuchas egui4,
Ca lo Giupponi5 and Fe dinando Villa1,2
Abs ac
P og ess in key social‑ecological challenges o he global en i onmen al agenda (e.g., clima e change, biodi e si y
conse a ion, Sus ainable De elopmen Goals) is hampe ed by a lack o in eg a ion and syn hesis o exis ing scien i ic
e idence. Facing a as ‑inc easing olume o da a, in o ma ion emains compa men alized o p e‑de ined scales and
ields, a ely building i s way up o collec i e knowledge. Today’s dis ibu ed co pus o human in elligence, including
he scien i ic publica ion sys em, canno be exploi ed wi h he e iciency needed o mee cu en e idence syn hesis
challenges; compu e ‑based in elligence could assis his ask. A i icial In elligence (AI)‑based app oaches unde lain
by seman ics and machine easoning o e a cons uc i e way o wa d, bu depend on g ea e unde s anding o hese
echnologies by he science and policy communi ies and coo dina ion o hei use. By labelling web‑based scien i ic
in o ma ion o become eadable by bo h humans and compu e s, machines can sea ch, o ganize, euse, combine and
syn hesize in o ma ion quickly and in no el ways. Mode n open science in as uc u e—i.e., public da a and model
eposi o ies—is a use ul s a ing poin , bu wi hou sha ed seman ics and common s anda ds o machine ac ionable
da a and models, ou collec i e abili y o build, g ow, and sha e a collec i e knowledge base will emain limi ed. The
applica ion o seman ic and machine easoning echnologies by a b oad communi y o scien is s and decision mak‑
e s will a ou open syn hesis o con ibu e and euse knowledge and apply i owa d decision making.
Keywo ds: Global challenges, Sus ainabili y, A i icial in elligence, Seman ics, Knowledge in eg a ion and syn hesis
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Complex global issues, agmen ed knowledge
The global en i onmen al agenda includes di e se in e -
na ionally ag eed-upon goals encompassing a ied social
and ecological challenges (e.g., clima e change, biodi e -
si y conse a ion, economic coope a ion, mig a ion and
mos ecen ly, pandemic esponse). Almos e e y na ion,
non-go e nmen al o ganiza ion and la ge co po a ion
pa icipa es in ini ia i es add essing one o mo e o hese
policy goals. Howe e , ou abili y o deli e imely and
accu a e scien i ic e idence o add ess hese p oblems
emains limi ed.
The lack o angible p og ess ac oss hese global
challenges ela es in pa o hei na u e as “wicked
p oblems”—in e wined, mul is akeholde and wi h
po en ial solu ions dependen on subjec i e, compe -
ing in e es s. Fu he , igh ly linked issues like clima e
change and biodi e si y loss a e deal wi h in sepa a e
policy o ums (e.g., Con en ion on Biological Di e si y
(CBD), In e go e nmen al Panel on Clima e Change
(IPCC), In e go e nmen al Science-Policy Pla o m
on Biodi e si y and Ecosys em Se ices (IPBES)) and
s a egies (e.g., Eu opean Union Biodi e si y S a egy,
Eu opean Union Adap a ion S a egy). Add essing
hem e icien ly equi es unp eceden ed in eg a ion
and syn hesis o e idence (including da a and mod-
els p oduced by he scien i ic communi y, bu also
adi ional and s akeholde knowledge) ha can lead
Open Access
En i onmen al E idence
*Co espondence: s e ano[email p o ec ed]
1 Basque Cen e o Clima e Change (BC3), Scien i ic Campus
o he Uni e si y o he Basque Coun y, Sede Building 1, 1s loo , Ba io
Sa iena S/N, 48940 Leioa, Bizkaia, Spain
Full lis o au ho in o ma ion is a ailable a he end o he a icle
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Balbie al. En i onmen al E idence (2022) 11:5
o b oadly sha ed solu ions among a di e se ange o
s akeholde s. He e, we discuss how he cu en lack
o knowledge in eg a ion and e idence syn hesis p o-
ocols and echnologies is a c i ical cons ain limi ing
he design and implemen a ion o policies o suppo
global sus ainabili y e o s; o he ba ie s including
lack o adequa e mul iscale go e nance, powe asym-
me y, poli ical will, he digi al di ide [1] and isks o
une hical A i icial In elligence (AI) use [2, 3] all ou -
side he scope o his a icle.
Cu en ly, we a e wi nessing inc eased demand
o open science h ough he p omo ion o he FAIR
p inciples aimed a making da a and models Findable,
Accessible, In e ope able and Reusable [4]. Howe e ,
despi e ad ances in open da a and scien i ic me hods,
ools enabling in e ope abili y (i.e., he “I” in FAIR)
emain ime- and esou ce-in ensi e [5]. Fu he , open
science has been incomple ely adop ed wi hin he
ield o e idence syn hesis [6, 7]. As olumes o da a
inc ease apidly, in o ma ion euse emains compa -
men alized wi hin p e-de ined scales and ields, oo
a ely building i s way up o collec i e knowledge.
Addi ionally, no wi hs anding he apid g ow h o
in e disciplina y science, schola s a e s ill o en incen-
i ized o s udy pa icula scien i ic ields using disci-
plina y me hods and wo ld iews. Despi e subs an ial
p og ess, ba ie s o in e disciplina i y emain in
unding, publishing, and communica ion, which can
limi collabo a ion and knowledge sha ing [8]. In p o-
posing he idea o an open syn hesis communi y, Nak-
agawa e al. [7] no e he impossibili y o keeping up
wi h he deluge o scien i ic in o ma ion, hus me hods
a e needed o au oma e he syn hesis o esea ch e i-
dence, while simul aneously espec ing i s complexi y.
Indeed, knowledge in eg a ion and e idence syn he-
sis a he speed and dep h equi ed by he global en i-
onmen al agenda lie beyond he capaci y o oday’s
dis ibu ed human in elligence and can bene i om
he assis ance o compu e -based in elligence. To his
end, we a gue ha an AI- acili a ed app oach based on
seman ics and machine easoning (see Box1 o key
de ini ions) o e s a easible pa h o wa d o connec
he da a and digi al echnologies ha a e now held by
he academic, public and p i a e sec o s, so hey can
gene a e eal- ime insigh abou he s a e o he plane
a any scale. Howe e , such an app oach will equi e
coo dina ion ac oss he scien i ic communi y o c e-
a e, implemen and deli e uly FAIR scien i ic wo k-
lows o decision-making.
Box1. De ini ion o key concep s in his a icle
A i icial In elligence (AI): he science and enginee ing unde lying
he de elopmen o machines, especially compu e p og ams, capa‑
ble o pe o ming ac i i ies no mally hough o equi e in elligence.
AI encompasses app oaches including machine easoning, seman ic
anno a ion, machine lea ning, and o he s
In e ope abili y: he abili y o da a o ools om independen
esou ces o in eg a e o wo k oge he wi h minimal e o [4].
In e ope abili y can be achie ed wi h compa ible da a o ma s and
communica ion p o ocols (syn ac ic in e ope abili y) o da a ans e s
whe e a ecei ing sys em can p ope ly iden i y he meaning o
exchanged da a, eusing i app op ia ely (seman ic in e ope abili y
[11])
Knowledge in eg a ion: he p ocess o app op ia ely combining
independen ly p oduced scien i ic da a and models, by knowing
when, whe e and how o app op ia ely e‑use hem
Machine lea ning: he use o a ious algo i hms o unco e pa e ns
(e.g., co ela ion o clus e ing) in la ge da ase s. Wi hou s uc u ed
inpu s o ex ac pa e ns, machine‑lea ning sys ems canno sol e
new p oblems ha ha e no appa en ela ion o hei p io knowl‑
edge. Machine lea ning is cu en ly he mos widely used o m o AI
Machine easoning (i.e., machine-ope a ed logical in e ence
using o malized seman ics): applied o a seman ically anno a ed
knowledge base, machine easoning can suppo au oma ed alida‑
ion and linking o da a and models using logic o assemble hem
in o use ul s uc u es o compu a ion. Reasoning sys ems can ackle
new p oblems and build highe ‑le el knowledge using deduc i e and
induc i e easoning
Seman ics: he o maliza ion o knowledge in e ms o logical decla‑
a ions and axioms, collec ed in o on ologies (which de ine concep s
and he ela ions be ween hem), b eaking knowledge in o modula
componen s. Seman ic anno a ion can label scien i ic da a and
models wi h well‑de ined ca ego ies linked by clea ly bounded logical
ela ionships and can play a key ole in knowledge in eg a ion
A seman ic web o knowledge
Ou wo ld is unde going d ama ic digi al ans o ma-
ions wi h da a gene a ed a a ne e -be o e seen ol-
ume and eloci y [1, 2]. These include da a gene a ed
by mobile de ices, sa elli e and g ound senso s, social
media and ci izen-science pla o ms, coupled wi h cloud
and high-pe o mance compu ing and machine lea ning.
Despi e hese echnological, scien i ic and socie al de el-
opmen s, we a e no keeping pace wi h humani y’s g ea -
es challenges o p og ess owa ds solu ions.
Addi ionally, al hough ou unde s anding o plane a y-
scale p ocesses has imp o ed, we a e a om being able
o accu a ely ack key dynamics and c i ical h esholds
ac oss di e se scales and d i e s. Key p ocesses, en i ies
(e.g., na ions, wa e sheds, households, ecological com-
muni ies) and hei in e dependencies ac oss scales a e
a oo complica ed o indi idual human b ains o disen-
angle [9]. Simul aneously, oday’s eposi o ies o human
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Balbie al. En i onmen al E idence (2022) 11:5
in elligence, such as he scien i ic publica ion sys em, all
sho in connec ing he pieces o knowledge p oduced by
di e en ields. AI assis ance o e s a pa h o wa d.
To p o ide needed decision suppo , AI mus ul i-
ma ely simula e Ea h as a eal- ime, dynamic sys em
composed o nes ed social-ecological sys ems. A “digi-
al win Ea h” has been included in ecen communica-
ions on he Eu opean G een New Deal [2]. The idea o
building a simula ion o he plane has been p oposed in
di e en o ms by global (e.g., UN En i onmen , G oup
on Ea h Obse a ions), Eu opean and U.S. ins i u-
ions (e.g., Eu opean Commission and Eu opean Spa ial
Agency, he U.S. Geological Su ey and NASA), and he
p i a e sec o (e.g., Mic oso AI o Ea h, Google Ea h
Engine). Howe e , hese a e mos ly unde s ood as mas-
si e machine-lea ning e o s buil on Ea h obse a-
ions om a wide ange o sou ces, wi h limi ed a en ion
paid o seman ics and machine easoning. Recen ly, a
global digi al ecosys em o he plane was p oposed by
he UN En i onmen P og amme as “a complex dis ib-
u ed ne wo k” consis ing o ou key elemen s: (1) da a,
(2) algo i hms and analy ics (i.e., models), (3) suppo ing
echnological in as uc u e and (4) insigh s and applica-
ions [10]. A p ima y echnological bo leneck in build-
ing such cybe in as uc u es, which aim o b ing da a,
models and p ocessing powe oge he in a ious clouds,
is how o make independen ly p oduced da a and models
seamlessly in e ope able?
We a gue o a solu ion buil upon seman ics and
machine easoning [11, 12] (see Box 1). AI esea ch
poin s owa d a con e gence o echnologies (machine
easoning and machine lea ning, geospa ial in elligence,
da a analy ics and isualiza ion, senso s and sma con-
nec ed objec s) o sus ain go e nance pla o ms in na u-
al and social sys ems [13]. Machine easoning is d i en
by ac s and knowledge ha can be used o alida e
and link in o ma ion using logical in e ence [14]. Con-
cep s, en i ies, hei ela ionships and ( o some ex en )
beha iou s a e desc ibed in sha ed documen s (on olo-
gies) ha es ablish a logical ounda ion o consis en ly
anno a e web-accessible da a and model esou ces. This
knowledge base, pai ed wi h AI, could b ing he FAIR
p inciples o ull ui ion. Such AI can help ha ness he
complexi y o in eg a ing independen ly p oduced da a
and models wi h he goal o maximizing human well-
being and es o ing ecosys em unc ioning [15]. Mul i-
disciplina y seman ics ha a e explici ly enginee ed o
suppo easoning can make human knowledge in e op-
e able a a la ge scale and in dis ibu ed ashion, so ha
machines can assemble i o add ess complex social-eco-
logical issues. Widesp ead use o seman ics would as ly
imp o e he s a us quo, whe e inconsis en and imp ecise
use o e ms ac oss di e en ields impedes he syn hesis
o scien i ic e idence (e.g., [16]).
By labelling pee - e iewed, web-based scien i ic in o -
ma ion in ways eadable by bo h humans and compu -
e s, and using common s anda ds o machine-ac ionable
da a and models, machines can sea ch, o ganize, euse
and combine in o ma ion quickly and in no el ways—i.e.,
a seman ic web o knowledge [17, 18]. Achie ing his will
equi e se e al ac ions on he pa o scien is s ha go
beyond he s a e o he p ac ice o oday’s open science.
Fo example, he A i icial In elligence o En i onmen
and Sus ainabili y P ojec (ARIES, [19]) desc ibed below
p o ides in as uc u e o enable hese s eps. Speci i-
cally, key elemen s in ARIES enable (1) da a and model
de elope s o expose and main ain knowledge esou ces
as independen ly hos ed and open web se ices using
ne wo ked a chi ec u e, open s anda ds and applica ion
p og amming in e aces (APIs); (2) consis en seman-
ic anno a ion p ac ices ha can be applied by da a and
model de elope s, who can concu en ly pa icipa e in
he de elopmen o on ologies, while p oducing mo e
modula models ca ying documen a ion and app op i-
a e euse condi ions; and (3) a ision o a pee - o-pee
ne wo k hos ing con en a ailable o machine-ac ion-
able syn hesis, wi h ins i u ions main aining in e ope -
able da a and model esou ces o e ime. Mo e de ails on
each o hese s eps can be ound in Villa e al. [20].
This app oach connec s exis ing, web-accessible da a
and models, so ha new mul idisciplina y scien i ic
knowledge can be gene a ed om hem on demand,
complemen ing much slowe human-d i en model cou-
pling and euse [5]. AI-suppo ed, on- he- ly assem-
bly o scien i ic wo k lows enables he inco po a ion o
newly p oduced da a sou ces as hey become a ailable
on he ne wo k, educing la ency and p o iding a pa h
owa d much needed nea - eal- ime modelling. Widely
used seman ics call o open, anspa en and well-doc-
umen ed models, o cing a simple and modula model
coding s yle whe e encapsula ed documen a ion can be
made manda o y. In his way, in eg a ed compu a ional
wo k lows can collec and p ocess in o ma ion abou
each indi idually documen ed modelling componen ,
deli e ing ully anspa en assessmen s o model use s
[20].
In he ace o widesp ead use o , and publici y o ,
“big da a-d i en” machine lea ning [9], we belie e wide
unde s anding and use o seman ics and machine ea-
soning in scien i ic modelling is c i ical o add essing
oday’s sus ainabili y challenges. App oaches such as
ARIES ha e demons a ed how seman ics can maximize
da a and model eusabili y and in e ope abili y when
assessing ecosys em se ices and, mo e gene ally, in
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Balbie al. En i onmen al E idence (2022) 11:5
modelling complex human-na u e in e ac ions and hei
consequences.
No ably, ARIES has been applied o he Sys em o
En i onmen al Economic Accoun ing (SEEA)—an
in e na ional s a is ical s anda d used o measu e link-
ages be ween na ional economic accoun s and na u al
capi al s ocks and ecosys em se ice lows in physi-
cal and mone a y e ms, as well as in o ma ion on he
ex en and condi ion o ecosys ems [21]. ARIES o
SEEA was eleased in Ap il 2021, and i is accessible a
h ps:// seea. un. o g/ con e n / a ies- o - seea. I p o ides
a common pla o m o make da a and models in e op-
e able and imp o e he abili y o Na ional S a is ical
O ices o au oma e he compila ion o en i onmen-
al-economic accoun s and ela ed indica o s, which
equi es he abili y o in eg a e na ional s a is ics and
spa ial da a and models. ARIES o SEEA hus dem-
ons a es a pa h o wa d o be e syn hesizing he
in o ma ion equi ed o moni o complex linked social-
ecological sys ems h ough indica o s such as he Sus-
ainable De elopmen Goals.
Seman ic-d i en in eg a ion echnologies, such as
ARIES, o e six c i ically needed ad an ages o wen y-
i s cen u y in e disciplina y science and decision-mak-
ing, and pionee a new gene a ion o dis ibu ed digi al
in as uc u e o in eg a e independen ly p oduced da a
and models se ed online—a web o scien i ic obse a-
ions wi h he capabili y o:
1. Combine independen ly p oduced scien i ic p oduc s
in o wo k lows ha would be oo complex o indi-
idual humans o concei e, alida e and na iga e.
2. In eg a e di e en modelling pa adigms om sim-
ple (e.g., de e minis ic and p obabilis ic models) o
complex app oaches (e.g., agen -based and ne wo ks)
depending on con ex and scale.
3. Rescale sma ly ac oss scales, om local o global,
p omo ing adap i e solu ions ha a e au oma ically
cus omized o he scale o obse a ion.
4. Flexibly inco po a e he bes -a ailable knowledge,
om cu a ed global public da ase s o “big da a” o
use -p o ided da a.
5. Adop common, non-ambiguous seman ics in bo h
he implemen a ion and deli e y o p oduc s.
6. T ack quali y and unce ain y h oughou modelling
wo k lows.
Towa d aglobal digi al commons
Today’s open science in as uc u e—public da a and
model eposi o ies—p o ides an impo an s a ing
poin owa d he ision o an in eg a ed knowledge
Funde s
• Funding, incen i es, guidance, aining
ma e ials o esea che s
• Adap open-da a equi emen s o
maximize in e ope abili y & eusabili y
Knowledge enginee s Indi idual scien is s
P ac i ione s
(go e nmen , NGOs, p i a e sec o )
Use & con ibu e o seman ic web:
• Desc ibe da a & model elemen s using seman ics
• De ine app op ia e da a & model
euse condi ions
• Ins an ly access eal- ime scien i ic
knowledge o decision making
• Co-c ea e knowledge wi h scien is s
Collabo a e wi h scien is s
on seman ics & ooling
Da a & code eposi o ies
De elop & apply bes p ac ices o
ully FAIR scien i ic knowledge
Fig. 1 Roles in he ansi ion o a seman ic web o knowledge
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Balbie al. En i onmen al E idence (2022) 11:5
landscape ou lined abo e. Howe e , he lack o sha ed
seman ics hinde s ou collec i e abili y o ully exploi
and con inuously expand he exis ing knowledge base.
Coo dina ion ac oss he en i e scien i ic communi y
will be needed o achie e widesp ead use o a sha ed
seman ic sys em (Fig. 1). This will en ail (1) incen-
i es om unde s o c ea ion and use o seman ically
in e ope able sys ems, d i ing (2) subs an ially close
collabo a ion be ween domain scien is s, knowledge
enginee s and nex -gene a ion da a and code eposi o-
ies, which leads o (3) e e yday, AI-assis ed use o he
g owing knowledge base by bo h scien is s and deci-
sion make s. Wi h subs an ially lowe ed ba ie s o
non-seman ic expe s o con ibu e knowledge, and AI
bea ing he la ges sha e o he da a and model in e -
ope abili y bu den, unp eceden ed access o connec ed
scien i ic knowledge should be possible. The use o
seman ics in modelling is mos powe ul as an in en-
ional, collabo a i e p ocess ha e ec i ely in eg a es
he knowledge o indi idual scien is s and da a p o id-
e s. In o he wo ds, modelling p ocesses and p oduc s,
and he seman ics o desc ibe hem, should be e ed
by a la ge and mul idisciplina y scien i ic communi y
du ing hei de elopmen . Th ough a seman ics-d i en
app oach, he scien i ic communi y can suppo he
en i onmen al agenda by con ibu ing o a global digi-
al commons o da a and models in a Wikipedia-like
ashion.
A wo king mul i-scale Ea h sys em pla o m, dedi-
ca ed o e idence syn hesis o moni o ing he global
social-ecological challenges, could be eached by gen-
e alizing obse ed pa e ns based on da a collec ed by
well-es ablished ne wo ks (e.g., om Long Te m Eco-
logical Resea ch s a ions). This can be ini ially achie ed
h ough a ep esen a i e se o case s udies ha can se e
as a che ypes o machine lea ning and ans e ing he
knowledge acqui ed wi h da a-d i en s a is ical me h-
ods. Rela i e o he s a us quo o manual model coupling
and AI ocused solely on machine lea ning, seman ic
knowledge in eg a ion o e s a pa h o be e add ess
long-s anding challenges ela ed o he explo a ion o
al e na i e u u es, ipping poin s, and discon inui ies.
Fu he , such a global pla o m could inco po a e mul i-
dimensional alues, including he e ogeneous s akehold-
e s’ p e e ences, using in e ac i e echnologies (e.g., da a
iewe s, g aphical edi o s), which can accoun o subjec-
i e p e e ences when in e p e ing model ou pu s.
Nakagawa e al. [7] desc ibe a ision o how imp o ed
in e ope abili y can help uel an “e idence e olu ion” in
which old and new e idence can be quickly and ans-
pa en ly syn hesized—a ask o which a seman ic web
o knowledge is well sui ed. As a scien i ic communi y,
ou main challenge is o quickly adop and p o ide an
in eg a ed and scalable solu ion o suppo decision-
making o a mo e sus ainable plane , while na iga ing
as -mo ing and in e connec ed global c ises. Seman ics
and machine easoning o e a p o en way o wa d, bu
he bene i s hey o e o syn hesis u gen ly equi e mo e
widesp ead unde s anding and coo dina ion o hei use
ac oss he scien i ic and policy communi ies.
Abb e ia ions
AI: A i icial In elligence; API: Applica ion p og amming in e ace; ARIES:
A i icial In elligence o En i onmen and Sus ainabili y; CBD: Con en ion on
Biological Di e si y; EC: Eu opean Commission; FAIR: Findable, accessible, in e ‑
ope able, eusable; IPCC: In e go e nmen al panel on clima e change; IPBES:
In e go e nmen al science‑policy pla o m on biodi e si y and ecosys em
se ices; NASA: Na ional ae onau ics and space adminis a ion; SEEA: Sys em
o en i onmen al economic accoun ing; UN: Uni ed Na ions; US: Uni ed S a es.
Acknowledgemen s
The au ho s wish o hank all pas and p esen con ibu o s o he ARIES p o‑
jec . Any use o ade, i m, o p oduc names is o desc ip i e pu poses only
and does no imply endo semen by he U.S. Go e nmen .
Au ho s’ con ibu ions
SB de eloped he manusc ip idea, FV is he PI o he p ojec inspi ing his
a icle, KB & AM wo ked ex ensi ely on he manusc ip and de eloped Fig. 1,
all au ho s con ibu ed o he p ojec and he manusc ip . All au ho s ead and
app o ed he inal manusc ip .
Funding
This esea ch is suppo ed by he Basque Go e nmen h ough he BERC
2018–2021 p og am and by he Ike zaile Dok o een zako Hobekun za ako
dok o e za‑ondoko P og ama and by Spanish Minis y o Economy and Com‑
pe i i eness MINECO h ough BC3 Ma ía de Maez u excellence acc edi a ion
MDM‑2017‑0714. Suppo o KB’s ime was p o ided by he U.S. Geological
Su ey Land Change Science P og am.
A ailabili y o da a and ma e ials
Da a sha ing is no applicable o his a icle as no da ase s we e gene a ed o
analysed du ing he cu en s udy.
Decla a ions
E hics app o al and consen o pa icipa e
No applicable.
Consen o publica ion
No applicable.
Compe ing in e es s
The au ho s decla e ha hey ha e no compe ing in e es s.
Au ho de ails
1 Basque Cen e o Clima e Change (BC3), Scien i ic Campus o he Uni e ‑
si y o he Basque Coun y, Sede Building 1, 1s loo , Ba io Sa iena S/N,
48940 Leioa, Bizkaia, Spain. 2 IKERBASQUE, Basque Founda ion o Science,
Plaza Euskadi, 5, 48009 Bilbao, Spain. 3 U.S. Geological Su ey, Geosciences
and En i onmen al Change Science Cen e , Den e , CO, USA. 4 Wo ld Wildli e
Fund, Washing on, DC, USA. 5 Depa men o Economics, Ca’ Fosca i Uni e si y
o Venice, Venice, I aly.
Recei ed: 11 Oc obe 2021 Accep ed: 30 Janua y 2022
Page 6 o 6
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Publishe ’s No e
Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in pub‑
lished maps and ins i u ional a ilia ions.
