E alua ing common indica o s o assessing ma ine 1
p o ec ed a ea e ec s on conse a ion and ca ches 2
Daniel O ando 3
Co i Lopazanski 4
1
Ti le Page5
Manusc ip i le: E alua ing common indica o s o assessing ma ine p o ec ed a ea e ec s on
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conse a ion and ca ches7
Sho Ti le: E alua ing common indica o s o MPA e ec s8
Au ho s: Daniel O ando1* & Co i Lopazanski2,3
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1
In e -Ame ican T opical Tuna Commission, Ecosys em & Byca ch G oup, 8901 La Jolla Sho es
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D i e, La Jolla, CA, 92102, USA11
2
Duke Uni e si y Ma ine Labo a o y,Nicholas School o he En i onmen Beau o , NC, 28516,
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USA13
3
Uni e si y o Cali o nia San a Ba ba a, B en School o En i onmen al Science & Managemen ,
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San a Ba ba a, CA, 93106, USA15
*Co esponding au ho : [email p o ec ed]16
Open Resea ch s a emen : No da a we e collec ed o his s udy. This submission uses no el code,
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which is p o ided, pe ou equi emen s, in an ex e nal eposi o y o be e alua ed du ing he pee
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e iew p ocess. The link o his ex e nal eposi o y is h ps://gi hub.com/DanO ando/mpa-e ec s-19
and-indica o s. A p e-p in a chi e o he code along wi h all esul s needed o ep oduce his pape
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is also a ailable a Zenodo h ps://doi.o g/10.5281/zenodo.17308920 . This Zenodo eposi o y will
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be upg aded o a pe manen a chi e o he inal esul s and pape upon publica ion.22
Key Wo ds: Ma ine P o ec ed A eas, Conse a ion e ec i eness, Bio-economic modeling, P o-
23
ec ed a ea e alua ion, Fishe ies managemen 24
2
Abs ac 824
Ma ine p o ec ed a eas (MPAs) a e spa ial managemen ools designed o ideally bene i di e se
825
conse a ion and social objec i es, including biodi e si y, ood secu i y, and clima e esilience.
826
Because hese objec i es a e o en di icul o measu e di ec ly, e alua ions o MPAs ypically
827
ely on simpli ied empi ical indica o s—such as biomass inside e sus ou side MPAs, o g adien s
828
in biomass ou side bounda ies—as p oxies o b oade MPA e ec s. Ye in complex social–
829
ecological sys ems, e en well-measu ed indica o s may ail o cap u e he ue causal e ec s o
830
MPAs. 831
Using a la ge-scale simula ion amewo k, we explo ed a iabili y in MPA e ec s and assessed
832
whe he common indica o s eliably acked in ended objec i es o p o ec ed a eas. We show ha
833
simila ly sized MPAs can p oduce as ly di e en e ec s o conse a ion (gene ally posi i e) and
834
ishe y ca ches (mo e equen ly nega i e) depending on ac o s such as ishing lee dynamics
835
and habi a he e ogenei y wi hin and ac oss species. The deg ee o co ela ion be ween common
836
empi ical indica o s o MPA pe o mance and he ue e ec s o an MPA also a ied widely.
837
Indica o s such as Biomass Densi y Response Ra ios we e posi i ely co ela ed wi h he e ec o
838
MPAs on biomass inside hei bo de s (Spea man’s
𝜌
= 0.56), bu sligh ly nega i ely co ela ed
839
wi h he e ec o MPAs on o al ishe y ca ches (
𝜌
= -0.07). Common indica o s o spillo e , such
840
as g adien s in biomass densi y nea MPAs ela i e o a om MPAs, had li le co ela ion wi h
841
any simula ed conse a ion o ca ch ou come (absolu e alue o Spea man’s 𝜌all ≤0.2). 842
The high a iabili y in simula ed MPA e ec s highligh s he impo ance o e ec i e ecological
843
and economic moni o ing p og ams. We ound ha many indica o s cu en ly measu ed in and
844
a ound MPAs may con ain li le in o ma ion on mac o-le el e ec s such as o al changes in ish
845
popula ions o ishe y ca ches. The nex gene a ion o MPA esea ch needs in e disciplina y
846
esea ch o de elop p ac ical and eliable me hods o acking he e ec s o MPAs. Doing so will
847
help ensu e ha he planned apid and global expansion o MPAs has he bes chance o deli e ing
848
posi i e and equi able ou comes o na u e and people. 849
45
In oduc ion 25
Va ious o ms o spa ial managemen ha e been used o conse e and manage ma ine ecosys ems
26
ac oss cul u es h oughou human his o y (e.g. Johannes 2002). The 1990s ma ked a pe iod o
27
expanded in e es in bo h he science and use o spa ial managemen ools, speci ically he gene al
28
concep o “ma ine p o ec ed a eas” (MPAs) (Ca e al. 2019; Humph eys and Cla k 2020). MPAs
29
a e a eas o he ocean whe e human ac i i ies a e es ic ed o p ohibi ed o p omo e biodi e si y
30
conse a ion and po en ially achie e o he complemen a y objec i es (G o ud-Col e e al. 2021).
31
These objec i es can include p ese ing cul u al he i age, ebuilding popula ions inside p o ec ed
32
bo de s, p o iding conse a ion bene i s ou side in ished wa e s, and suppo ing su ounding
33
ishe ies (Gaines e al. 2010). In e na ional mo emen s such as he Mon eal–Kunming Global
34
Biodi e si y F amewo k’s call o p o ec ion o 30% o he oceans, and he Ag eemen unde
35
he Uni ed Na ions Con en ion on he Law o he Sea on he Conse a ion and Sus ainable Use
36
o Ma ine Biological Di e si y o A eas Beyond Na ional Ju isdic ion ( he High Seas T ea y o
37
BBNJ) ha e p o ided subs an ial momen um o he expanded use o MPAs in he wo ld’s oceans
38
in pu sui o hese goals. 39
O e 9% o he ocean is now co e ed by MPAs (as o Oc obe 2025; Wo ld Da abase o P o ec ed
40
A eas), and he ecen global expansion o MPAs has been ma ched by g owing e o s o empi -
41
ically e alua e hei pe o mance (e.g. Osenbe g e al. 2011; Edga e al. 2014; Di Lo enzo e
42
al. 2020; Lynham and Villaseño -De bez 2024; Hop e al. 2024) . Since many o he bene i s
43
ha MPAs a e expec ed o deli e a e di icul o measu e di ec ly, e alua ions o MPA e ec s
44
o en ely on acking simple indica o s as p oxies o b oade MPA e ec s (Pelle ie 2011; Hop 45
e al. 2024). These indica o s enable assessmen s o be ca ied ou ac oss di e se con ex s, and
46
ha e become a ounda ional ool o a emp ing o unde s and MPA e ec s in bo h scien i ic and
47
policy se ings. Many indica o s compa e a ibu es wi hin MPAs o nea by unp o ec ed a eas
48
[i.e., esponse a ios; Smi h e al. (2024); Les e e al. (2009)], o om unp o ec ed a eas ha a e
49
nea o MPA bo de s compa ed o hose a he away (i.e., g adien s, Halpe n, Les e , and Kellne 50
3
2009). Mo e causally- obus app oaches inco po a e measu emen s bo h be o e and a e MPA
51
implemen a ion [i.e., be o e-a e -con ol-impac o BACI; Medo , Lynham, and Rayno (2022);
52
Lynham and Villaseño -De bez (2024); Ke , K i ze , and Cad in (2019); O ando e al. (2021)].53
While hese empi ical indica o s di e in hei complexi y, each is p esumably in ended o de e -
54
mine he e ec o he MPA on a pa icula obse ed me ic: highe biomass in MPAs compa ed
55
o unp o ec ed a eas is in e p e ed as conse a ion gains wi hin MPA bo de s (Smi h e al. 2024;
56
Les e e al. 2009), while g adien s in ishe ies ca ch o e o a e hough o signal e idence o
57
spillo e o adul o la al ish om he MPA o su ounding wa e s (Halpe n, Les e , and Kellne
58
2009; Medo , Lynham, and Rayno 2022; Lynham and Villaseño -De bez 2024; Robe s e al.
59
2001). Howe e , hese indica o esul s a e some imes hen assumed o also imply e idence o an
60
unobse ed e ec , such as changes in o al popula ion size o o al ishe ies ca ch.61
The assump ion ha speci ic indica o s can s and in o b oade and ha de o obse e ecological
62
o social e ec s has no been o ou knowledge comp ehensi ely es ed, despi e hei widesp ead
63
use ( hough wo ks such as Hop e al. (2024), Ke , K i ze , and Cad in (2019), and Hilbo n e al.
64
(2024) e alua ed speci ic combina ions o indica o s and e ec s). Unde s anding he eliabili y o
65
indica o s, and he a iabili y o he unde lying MPA e ec s hey aim o measu e, is c i ical o
66
in e p e ing exis ing e alua ions, designing e ec i e moni o ing s a egies, and making e idence-
67
in o med policy decisions.68
As MPA usage expands a ound he wo ld, es ablishing expec a ions a ound po en ial MPA e ec s
69
and ou abili y o accu a ely measu e hem is essen ial o acking p og ess owa ds ou comes
70
achie ed, no jus a ea p o ec ed. To ha end, his pape add esses wo ques ions. We i s anal-
71
ysed whe he MPA e ec s on conse a ion and ca ches a e likely o be a iable enough o jus i y
72
he need o empi ical moni o ing. We hen es ed he abili y o se e al commonly used empi ical
73
indica o s o e lec he ac ual e ec s o MPAs. We ound ha MPAs can ha e highly a iable
74
e ec s on conse a ion and ca ches depending on he speci ic dynamics o he sys em in ques ion,
75
and ha while some indica o s eliably ack his a iabili y in some MPA e ec s, many do no .76
4
Me hods 77
MPA Expe imen O e iew 78
We used a simula ion amewo k o model bo h he a iabili y o MPA e ec s and he pe o mance
79
o empi ical MPA indica o s. The gene al s uc u e o his simula ion expe imen is as ollows.
80
We c ea ed a ishe y sys em wi h a se o bio-economic ai s. We hen an an MPA expe imen
81
on ha sys em by gene a ing wo pai ed simula ions iden ical in e e y way, excep ha one
82
sys em con ained a no- ake MPA and he o he did no . We hen calcula ed he simula ed e ec s
83
o he MPA on a ange o objec i es based on he di e ences be ween he simula ion wi h an
84
MPA ela i e o he pai ed simula ion wi hou an MPA. Las ly, we calcula ed common empi ical
85
indica o s o MPA pe o mance om he simula ion wi h he MPA, and compa ed hese indica o s
86
o he ue simula ed e ec s. We hen epea ed his p ocess mul iple imes wi h di e en andomly
87
gene a ed ishe y s a es. The de ails o his p ocess a e explained below and illus a ed in Figu e 1.
88
Simula ing Fishe ies 89
Fishe ies sys ems we e simula ed using he ma lin model p esen ed in O ando e al. (2023) and
90
O ando (2025). ma lin simula es a use -speci ied numbe o age-s uc u ed ish popula ions ished
91
by a use -speci ied numbe o ishing lee s, all ope a ing in a wo-dimensional space. This spa ial
92
domain ep esen s he su ace a ea o he seascape, b oken up in o a se ies o “pa ches”, each
93
wi h a de ined spa ial a ea. Fo his pape , we modeled a 21 x 21 g id o pa ches ( o a o al o
94
441 pa ches), whe e each pa ch had a su ace a ea o 25KM
2
(5 km x 5km), o a o al simula ed
95
seascape a ea o 11,025 KM2.96
To explo e MPA e ec s and indica o s ac oss a ange o species, we modeled ou di e en species
97
a che ypes made om a se o ixed and a iable pa ame e s. These simula ed species we e b oadly
98
based on a una (Thunnus albaca es), sha k (Ca cha hinus alci o mis), g oupe (Epinephelus
99
uscogu a us), and ee ish (Se iola quinque adia a).Fixed pa ame e s included hose ela ing o
100
5
Figu e 1: Concep ual illus a ion o he p ocess o simula ing e ec s o MPAs, calcula ing
empi ical indica o s, and compa ing indica o s o e ec s. Colo ed polygons in S ep 2 indica e
habi a dis ibu ions o each simula ed species. See Me hods sec ion o de ailed explana ions o
hese s eps.
6
g ow h, mo ali y, ecundi y, ec ui men , and mo emen , wi h alues o each species based on
101
a ailable li e a u e and ou own judgemen (Table 1). We ixed hese pa ame e s in o de o no
102
c ea e un ealis ic combina ions o o example g ow h and mo ali y (P ince e al. 2015). 103
One o he key ea u es o his model is he abili y o simula e ish mo emen ac oss he wo-
104
dimensional seascape h ough bo h passi e di usion and ac i e axis in esponse o habi a g a-
105
dien s, in he manne o J. T. Tho son e al. (2021) (see O ando (2025)). Empi ical es ima es o
106
mo emen a es in he same uni s as hose used in ou simula ion model we e no eadily a ailable.
107
As such, we ixed mo emen a es o ec ui s and pos - ec ui ish such ha 95% o animals we e
108
wi hin a gi en linea dis ance o hei poin o o igin a e one yea o di usion and axis. We
109
g ouped hese dis ances in o low (2.5km), medium (25km), o high (250km) dispe sal dis ances,
110
and assigned hese dispe sal dis ances o he ec ui and pos - ec ui li e s ages o each o ou simu-
111
la ed species based on ou bes judgemen and a desi e o e lec a ange o mo emen dynamics
112
(e.g. seden a y adul s and highly dispe sed la ae, and ice e sa) (Table 1). 113
Fo each simula ion we augmen ed hese ixed pa ame e s wi h andom d aws o a iable pa ame-
114
e s ha desc ibe o he less-ce ain aspec s o he simula ion. These include he ecological ai s
115
such as he uni o mi y o species-le el habi a ac oss he simula ed seascape, he co ela ion in
116
habi a ac oss species, seasonal mo emen shi s, spawning agg ega ions, he iming o densi y
117
dependence, he deg ee o ec ui men de ia ion, and he empo al and c oss-species co ela ions
118
in ec ui men de ia es (Table 2). Va iable ai s also included ishe y cha ac e is ics such as he
119
magni ude o ishing p essu e on each species, he economic alue o each species o each lee ,
120
he p esence o ishing po s a ec ing spa ial ishing choices, and he selec i i y o each ishing
121
lee o each ished species (Table 2). 122
While all ai s can ha e an impac on simula ed e ec s, spa ial a iables a e pa icula ly impo an
123
o MPA e ec s, so we explain hem in mo e de ail he e. The simples assump ion one can make in
124
an MPA model is ha all else being equal ish biomass and ishing p essu e a e cons an in space
125
and ime, ba ing a policy in e en ion. Unde hese ci cums ances, in he absence o an MPA, ish
126
7
biomass and ishing e o a e always he same in each pa ch. Following MPA implemen a ion
127
in such a sys em, any di e ences in biomass o ish ca ches in any pa ch mus be due o he MPA
128
i sel .129
In eali y, ish popula ions can be he e ogeneously dis ibu ed in space a any gi en ime s ep,
130
and hei species dis ibu ions can exhibi posi i e, nega i e, o no co ela ions wi h o he ished
131
species in he egion. These spa ial dynamics o species dis ibu ions a e so p onounced in ma ine
132
sys ems ha an ex ensi e li e a u e o Species Dis ibu ion Models (SDMs) has de eloped o
133
desc ibe and model hem (J. T. Tho son and Ba ne 2017; Ka p e al. 2025; B odie e al. 2021)
134
(Lopez e al. 2024). This means ha an MPA placed on co e habi a can ha e e y di e en e ec s
135
om a equally sized MPA placed on ma ginal habi a . Simila ly, gi en ha no all species ha e
136
he same habi a dis ibu ions, an MPA placed on he co e habi a o one species may be p o ec ing
137
ma ginal habi a o ano he .138
Following me hods desc ibed in O ando e al. (2023) and O ando (2025), o each species wi hin
139
each simula ion we andomly se a pa ame e
𝜅
ha desc ibes he in insic he e ogenei y o hei
140
habi a ac oss he simula ed seascape; lowe alues o
𝜅
esul in a smoo he habi a dis ibu ion,
141
highe alues o
𝜅
esul in a mo e pa chy habi a dis ibu ion. A he same ime, we andomly
142
gene a ed a habi a co ela ion ma ix be ween each o he species in ha seascape in a gi en
143
simula ion. Using he me hods desc ibed in O ando (2025) based on J. T. Tho son and Ba ne
144
(2017), hese
𝜅
alues o each species, along wi h he co ela ion ma ix among all species,
145
a e used o cons uc a co a iance ma ix. which in u n is used o simula e habi a in space as
146
a unc ion o a mul i a ia e no mal dis ibu ion. We used his mul i a ia e no mal dis ibu ion
147
o gene a e andom d aws o species dis ibu ions ha can a y ac oss simula ions bo h in hei
148
he e ogenei y and in hei co ela ion ac oss species, wi h o example some simula ions esul ing
149
in ee ish and g oupe s ha ing pa hcy and highly posi i ely co ela ed habi a s, and in o he s
150
smoo h and highly nega i ely co ela ed habi a s.151
Along wi h habi a he e ogenei y, ou model accoun s o some o he complex beha io s o ishing
152
8
ex eme le els o deple ion a e a e in ma ine ish popula ions and p oduce ex eme esul s when
245
conside ing MPA e ec s on a pe cen age based scale. Simila ly, we emo ed any simula ions
246
in which any species had o al biomass alues less han one, o any ishing lee had ca ches less
247
han one. These simula ions we e emo ed since hey esul ed in imp obably high pe cen age
248
e ec sizes (e.g. inc eases in ca ches o o 900% when ca ches inc eased om 0.1 MT o 1 MT).
249
Pos - il e ing, he combina ion o each simula ed ishe y wi h each MPA design esul ed in a o al 250
o 10,896 unique MPA expe imen s. 251
Calcula ing Empi ical Indica o s 252
The p io s eps o simula ing a ishe y sys em unde a ange o MPA expe imen s c ea e a dis i-
253
bu ion o causal e ec s o MPAs on he selec ed me ics. We compa ed hese ue e ec s wi h
254
es ima es om common empi ical indica o s o MPA pe o mance. To isola e he undamen al
255
pe o mance o he indica o om ques ions a ound da a quali y and bias, all indica o s we e
256
calcula ed using he simula ed da a wi hou any obse a ion e o . 257
We e alua ed six empi ical indica o s commonly used o assess MPA pe o mance; Biomass
258
Densi y Be o e-A e -Con ol-Impac ( BACI), Biomass Densi y Response Ra io,Mean Leng h
259
Response Ra io,E o G adien ,Biomass Densi y G adien ,Biomass Densi y Be o e-A e G adi- 260
en (Table 3). These indica o s a y in hei unde lying esponse a iable (biomass densi y, mean
261
leng h, o ishing e o ), hei spa ial design (inside-ou side o nea - a ), and whe he hey include
262
empo al compa isons (be o e-a e s. a e -only). These indica o s we e selec ed o ep esen a
263
ange o app oaches desc ibed in he empi ical MPA li e a u e o in e conse a ion and ishe ies
264
e ec s. A ull desc ip ion o each indica o and i s s ylized equa ion is p o ided in Table 3.265
All models we e i using a log-no mal dis ibu ion, wi h he dependen a iable measu ed a he
266
esolu ion o species, pa ch, and ime s ep (and by lee whe e applicable). Coe icien s om hese
267
models can be in e p e ed as mul iplica i e e ec s; e ec sizes a e epo ed as pe cen age changes
268
using he ans o ma ion
100 × (𝑒𝛽𝑀𝑃𝐴 − 1)
. All indica o s we e calcula ed a he species le el,
269
15
as esul s we e obus o agg ega ing biomass o ca ch ac oss species (Figu e S30).270
Indica o s based on inside-ou side compa isons (e.g., Biomass Response Ra io, Biomass BACI,
271
Mean Leng h Response Ra io) equi e he selec ion o a compa able e e ence si e ou side he
272
MPA. In empi ical s udies, e e ence si es a e o en selec ed o ma ch he habi a and en i on-
273
men al cha ac e is ics wi hin he MPA (Chapman and K ame 1999; Claude and Guide i 2010;
274
Osenbe g e al. 2011; Smi h e al. 2024). We app oxima ed his e e ence si e selec ion app oach
275
by con olling o un ished biomass pe pa ch and weigh ing eg essions by dis ance om he MPA
276
bo de . Speci ically, we calcula ed he linea dis ance o each pa ch om he nea es MPA bo de
277
and weigh ed models such ha he he model pays he mos a en ion o pa ches deepes inside he278
MPA and hose a hes ou side he MPA.279
G adien (nea - a ) indica o s (e.g., E o G adien , Biomass G adien , Biomass Be o e-A e
280
G adien ) compa e me ics ou side o MPAs based on dis ance om MPA bo de s. Following he
281
gene al app oach o Medo , Lynham, and Rayno (2022) and Lynham and Villaseño -De bez
282
(2024), we calcula ed he linea dis ance om each pa ch om he MPA bo de , and assigned
283
pa ches in he lowes 20 h pe cen ile o dis ance as Nea and hose in he op 80 h pe cen ile as
284
Fa .Aswi hinside-ou side s yle analyses, we also con olled o un ished biomass pe species pe
285
pa ch.286
All models we e i using linea models in R (R Co e Team 2024). Fo each indica o , a iables
287
we e measu ed wi hou e o a he esolu ion o spa ial pa ch pand ime s ep . The spa ial eso-
288
lu ion o he sys em is 21 by 21 pa ches, meaning ha any gi en ime s ep has 441 pa ches. Fo
289
models i o a single ime s ep hen (e.g. Biomass Densi y Response Ra io), he numbe o da a
290
poin s used o i he model we e N = 441. Fo models i o mul iple ime s eps o be o e and a e ,
291
(e.g. Biomass Densi y BACI), he numbe o da a poin s used o i he model we e N = 882.292
16
Table 3: Gene al s uc u e o es ed indica o s o MPA e ec s. Bolded 𝛽pa ame e in Equa ion
column indica es pa ame e in e p e ed as he e ec o he MPA. B e e s o biomass, E e e s o
e o , ML e e s o mean leng h. INSIDE e e s o pa ches ploca ed inside MPA bo de s (whe he
be o e o a e MPA implemen a ion), OUTSIDE o pa ches ou side. AFTER e e s o ime pe iods
a e MPA implemen a ion, BEFORE is be o e MPA implemen a ion. NEAR a e pa ches p
ou side bu nea he bo de o an MPA (whe he be o e o a e MPA implemen a ion), FAR is
ou side and a he om he bo de o an MPA. No e ha all eg essions also include co a ia es o
un ished o al biomass ac oss all species in each pa ch p, and weigh ing by absolu e dis ance om
MPA bo de . Howe e we omi hese ancilla y e ms om he able o cla i y. Any indica o
lacking a ime e m is measu ed in he inal ime-s ep o he simula ion, 20 yea s ollowing MPA
implemen a ion.
Indica o
Es ima ed E ec and Key
Assump ions Example Use Equa ion
Biomass Densi y
Be o e-A e
-Con ol-Impac
(inside-ou side)
Ra io o change in Ba e
MPA implemen a ion
inside MPA ela i e o
ou side MPA. Assumes
Pa allel ends in Binside
and ou side MPA p e- and
pos -implemen a ion.
Hop e al. (2024)
𝑙𝑜𝑔(𝐵𝑝,𝑡)∼𝛽
0+
𝛽1𝐼𝑁𝑆𝐼𝐷𝐸𝑝+𝛽
2𝐴𝐹𝑇𝐸𝑅𝑡
+𝛽3
𝛽3
𝛽3𝐼𝑁𝑆𝐼𝐷𝐸𝑝× 𝐴𝐹𝑇𝐸𝑅𝑡
Biomass Densi y Response
Ra io (inside-ou side)
Pos -MPA Ra io o B
inside MPA ela i e o
ou side MPA. Assumes no
p e-exis ing di e ences in
Bbe ween inside and
ou side si es
Smi h e al. (2024) 𝑙𝑜𝑔(𝐵𝑝)∼
𝛽0+𝛽
1
𝛽1
𝛽1𝐼𝑁𝑆𝐼𝐷𝐸𝑝
Mean Leng h Response
Ra io (inside-ou side)
Pos -MPA a io o ML
inside MPA ela i e o
ou side MPA. Assumes no
p e-exis ing di e ences in
ML be ween inside and
ou side pa ches.
Halpe n (2003) 𝑙𝑜𝑔(𝑀𝐿𝑝)∼
𝛽0+𝛽
1
𝛽1
𝛽1𝐼𝑁𝑆𝐼𝐷𝐸𝑝
E o G adien (nea - a ) Pos -MPA a io o Enea
MPA ela i e o a .
Assumes no p e-exis ing
di e ences in Ebe ween
nea and a pa ches.
Goni e al. (2008) 𝑙𝑜𝑔(𝐸𝑝)∼
𝛽0+𝛽
1
𝛽1
𝛽1𝑁𝐸𝐴𝑅𝑝
17
Indica o
Es ima ed E ec and Key
Assump ions Example Use Equa ion
Biomass Densi y G adien
(nea - a )
Pos -MPA a io o Bnea
MPA ela i e o a om
MPA. Assumes no
p e-exis ing di e ences in
Bbe ween nea and a
pa ches.
Halpe n, Les e , and
Kellne (2009)
𝑙𝑜𝑔(𝐵𝑝)∼
𝛽0+𝛽
1
𝛽1
𝛽1𝑁𝐸𝐴𝑅𝑝
Biomass Densi y
Be o e-A e G adien
(nea - a )
Ra io o change in Ba e
MPA implemen a ion nea
o MPA ela i e o a om
MPA. Assumes Pa allel
ends in Bdensi y nea
and a om MPA p e- and
pos -implemen a ion.
Lynham and
Villaseño -De bez (2024)
𝑙𝑜𝑔(𝐵𝑝,𝑡)∼𝛽
0+
𝛽1𝑁𝐸𝐴𝑅𝑝+𝛽
2𝐴𝐹𝑇𝐸𝑅𝑡+
𝛽3
𝛽3
𝛽3𝑁𝐸𝐴𝑅𝑝× 𝐴𝐹𝑇 𝐸𝑅𝑡
We do no epo unce ain y o es ima es, as ou p ima y objec i e is o e alua e ela i e pe o -
293
mance ac oss indica o s a he han o make s a is ical in e ence. Gi en ha his is a simula ion-
294
based analysis wi h a bi a ily de ined sample sizes, con en ional s a is ical signi icance es ing is295
no meaning ul (Whi e e al. 2014), so we do no e alua e indica o s agains s a is ical signi icance
296
h esholds (e.g., p < 0.05). Simila ly, we do no accoun o s a is ical complexi ies such as spa io-
297
empo al clus e ing (J. T. Tho son and K is ensen 2024) o ze o-in la ion (Zuu 2009). Applied use
298
o empi ical indica o s would need o be ailo ed o he local con ex , ollowing bes p ac ices in
299
modeling and causal in e ence based on obse a ional da a om social-ecological sys ems.300
Cali o nia Response Ra io Case S udy301
The 10896 MPA expe imen s simula ed in his s udy we e gene a ed o e lec a ange o possible
302
bio-economic s a es and MPA design s a egies. Howe e , as a esul he e is no gua an ee ha
303
he dis ibu ion o simula ed MPA e ec s wi hin his ange is e lec i e o he alues seen in he
304
eal wo ld. To pa ly add ess his challenge, we c ea ed a subse o ou simula ions selec ed o
305
ma ch he es ima ed esponse a ios om a ne wo k o MPAs along he coas o Cali o nia, USA,
306
epo ed in Smi h e al. (2024). These esponse a ios a e epo ed a he le el o indi idual MPAs,
307
wi h sepa a e a ios o he o al biomass densi y o a ge ed and non- a ge ed ishes. In o de o
308
18
ma ch hese empi ical esponse a ios, we sampled 2500 o ou MPA expe imen s wi h eplacemen
309
using a weigh ing scheme designed o p oduce a dis ibu ion o Biomass Densi y Response Ra ios
310
ha ma ched hose epo ed in Smi h e al. (2024) (see Figu e S27 ). We es ic ed he candida e
311
simula ions o his p ocess o MPA sizes be ween 10% and 40%, and o mul i-species le els o
312
complexi y, o mo e closely e lec plausible scena ios o he Cali o nia sys em. This allowed us
313
o examine wha ange simula ed MPA e ec s could be associa ed wi h he dis ibu ion o Biomass
314
Densi y Response Ra ios measu ed in he Cali o nia MPAs s udied in Smi h e al. (2024). No e ha
315
we do no claim hese as being simula ions o he Cali o nia MPA sys em i sel , bu a he a se
316
o simula ions cons ained o ma ch a dis ibu ion o Biomass Densi y Response Ra ios seen in a
317
eal-wo ld sys em. 318
Compa ing MPA E ec s and Indica o s 319
The p io s eps p o ide simula ed MPA e ec s and indica o alues o each MPA expe imen .
320
We hen assessed he abili y o each indica o o ack each ou come using a ange o me ics.
321
We measu ed he co ela ion be ween each indica o and each ou come using Spea man’s ank
322
co ela ions (
𝜌
), gi en he po en ial o highly di e en scales be ween he indica o and he e ec s
323
and he po en ial o non-linea ela ionships be ween he indica o and he e ec s. We con e ed
324
hese ρ alues in o ρ
2
alues ha e lec he p opo ion o he ank-o de a iance in he ou come
325
ha is explained by he indica o . These Spea man’s ank co ela ion alues p o ide a gene al
326
sense o he ex en o which a highe indica o alue in one MPA expe imen and a lowe indica o
327
alue in ano he MPA expe imen co esponds o a ela i ely highe ou come alue a he i s
328
MPA expe imen and a ela i ely lowe ou come alue a he second MPA expe imen . 329
To assess how accu a ely each indica o e lec ed he ue ou come, we calcula ed he oo mean
330
squa ed e o (RMSE) and he mean e o (Bias) o each indica o -ou come pai . These me ics
331
a e exp essed in pe cen age poin s, no pe cen di e ences. Fo example, a Bias o -20 means
332
ha he indica o unde es ima ed he ue ou come by an a e age o 20 pe cen age poin s (e.g.,
333
19
es ima ing 30% when he ue alue is 50%), no ha i was 20% lowe . Simila ly an RMSE o 20
334
pe cen age poin s indica es ypical de ia ions o abou +/- 20 pe cen age poin s, no ha he e o
335
was wi hin 20% o he ue ou come.336
Rep oducing Resul s337
All code needed o ully ep oduce he esul s and manusc ip a e publicly a ailable a h ps:
338
//gi hub.com/DanO ando/mpa-e ec s-and-indica o s h ps://doi.o g/10.5281/zenodo.17308920.339
Resul s340
Va iabili y o MPA E ec s341
Ou simula ed ishe ies a ied in hei beha io as a unc ion o he ixed (Table 1) and a iable
342
(Table 2) simula ion pa ame e s. The deg ee o esul ing ishing p essu e, and subsequen deple-
343
ion, is one o he mos impo an de e minan s o MPA e ec s. The median deple ion (
𝐵/𝐵0
)
344
alue ac oss all ou included simula ions was 0.38, mean alue 0.41, anging om a minimum o
345
0.01and a maximum o 1.29 (no ing ha alues abo e 1 a e possible gi en ec ui men a ia ion).346
Ou simula ed MPAs had a wide ange o e ec s on species-le el biomass and ca ch, wi h bo h
347
posi i e and nega i e causal e ec s possible o all measu ed e ec s (Figu e 2). Inc easing MPA
348
size gene ally expanded he ange o possible e ec s. MPAs caused an inc ease in Biomass
349
Inside hei bo de s in nea ly all simula ions (96%), hough nega i e e ec s we e also obse ed
350
in 4%. In con as , Biomass Ou side MPA bo de s inc eased in 48% o simula ions, indica ing a
351
oughly e en likelihood o posi i e o nega i e e ec s on biomass beyond MPA bo de s ac oss
352
ou simula ions. This asymme y (consis en biomass gains inside and a iable e ec s ou side)
353
esul ed in a ne inc ease in To al Biomass in 88% o simula ions and a ne dec ease in 12% o
354
simula ions. Nega i e MPA e ec s o Biomass Inside and To al Biomass we e mo e common
355
when he MPA placemen s a egy was o a oid ishing and when ishing e o was displaced
356
20
by he MPA, as hese scena ios gene ally esul ed in he concen a ion o ishing e o in p ime
357
habi a . The possibili y o he MPA causing ne losses in biomass inc eased wi h he le el o
358
ishing p essu e (Figu e 2). 359
MPAs esul ed in a ne inc ease in species- and lee -le el Ca ch in 27% o simula ions, and a
360
ne dec ease in Ca ch in 73% o simula ions. Smalle e ec sizes on Ca ch (absolu e e ec sizes
361
less han 25%) we e much mo e common han la ge e ec sizes. Using he A oid Fishing MPA
362
s a egy esul ed in sligh ly lowe equency o ne ca ch losses han he Ta ge Fishing s a egy,
363
bu only sligh ly less so. While nega i e ca ch e ec s s ill occu ed ac oss all simula ed le els
364
o ishing p essu e, he numbe o simula ions wi h posi i e ca ch e ec s inc eased wi h he
365
coun e ac ual deg ee o ishing p essu e (Figu e 2). 366
21
To al Biomass Ca ch
Biomass Inside Biomass Ou side
0-15%
15-30%
30-60%
0-15%
15-30%
30-60%
-100%
0%
100%
≥200%
-100%
0%
100%
≥200%
-100%
0%
100%
≥200%
-100%
0%
100%
≥200%
Seascape in MPA
MPA E ec
B/B0 >50% 25-50% 0-25% Quan ile 100% 80% 50%
Figu e 2: Dis ibu ion o simula ion esul s ac oss MPA e ec s (panels), p opo ion o seascape in
MPA (columns), and le el o baseline deple ion in he absence o MPAs (colo s, biomass B
di ided by un ished biomasss B0). Y-axis shows he “MPA e ec ”, he pe cen change in he
ou come in ques ion caused by he MPA. Biomass Inside e e s o o al indi idual species biomass
inside MPA bo de s. Biomass Ou side e e s o o al indi idual species biomass ou side MPA
bo de s. To al Biomass e e s o o al indi idual species biomass bo h inside and ou side MPA
bo de s. Ca ch e e s o o al ca ch pe species and lee ou side he MPA.
22
Pe o mance o Empi ical Indica o s o MPA E ec s 367
The abili y o empi ical indica o s o ack MPA e ec s a ied widely (Figu e 3A). All h ee
368
inside-ou side indica o s had posi i e Spea man’s
𝜌
wi h Biomass Inside and To al Biomass,wi h
369
Biomass Densi y BACI ha ing he highes
𝜌
alues o bo h (
𝜌
= 0.64 and
𝜌
= 0.45 espec i ely),
370
ollowed by Biomass Densi y Response Ra io (
𝜌
= 0.56and
𝜌
= 0.38 espec i ely). All h ee
371
inside-ou side indica o s had nega i e Spea man’s
𝜌
alues wi h Biomass Ou side, meaning ha
372
simula ions wi h ela i ely highe biomass densi y esponse a ios we e associa ed wi h simula ions
373
wi h ela i ely lowe Biomass Ou side e ec s. 374
G adien indica o s exploi ing nea - a pa e ns a ound MPAs had much lowe Spea man’s
𝜌375
alues ac oss all e ec s (all
|𝜌| ≤ 0.2
), explaining almos none o he ank-le el a ia ion in any
376
o he e alua ed MPA e ec s. Among he nea - a indica o s, E o G adien s had he highes
377
co ela ion alues, (𝜌= 0.2 o Biomass Inside and 𝜌= 0.16 o To al Biomass) (Figu e 3). 378
None o he e alua ed indica o s we e meaning ully co ela ed wi h he e ec s o MPAs on o al
379
ca ches, hough all es ima ed co ela ions we e nega i e. Biomass Densi y BACI had he clea es
380
nega i e co ela ion wi h MPA e ec s on ca ch, wi h 𝜌 = −0.14 (Figu e 3). 381
The
𝜌
alues shown in Figu e 3indica e co ela ion be ween he indica o alue and he ou come
382
alue. These co ela ions ell us how eliably an indica o can be used o ank di e en MPAs in
383
e ms o pe o mance ela ed o a speci ic MPA e ec . We also examined how well aw indica o
384
alues ep esen ed aw MPA e ec s by calcula ing measu es o e o ( oo mean squa ed e o ) and
385
bias (mean absolu e e o ), whe e posi i e bias alues indica e ha , on a e age, indica o alues
386
we e highe han ue alues, and ice e sa. These me ics a e mo e use ul o de e mining how
387
well he alue o a gi en indica o a one MPA ansla es in o he alue o a gi en e ec s a he
388
same MPA. The a e age RMSE ac oss all indica o s was 93 pe cen age poin s, wi h an a e age
389
bias ac oss all indica o s o 13 pe cen age poin s. Mos indica o s we e posi i ely biased ela i e o
390
he ue e ec size, hough some indica o s we e nega i ely biased o Biomass Inside and To al
391
Biomass.392
23
All o he esul s shown in Figu e 3a e based compa isons a he le el o indi idual species and
393
whe e applicable lee s, o example compa ing he Biomass Densi y BACI alue o ee ish
394
o he ue e ec o he MPA on ee ish ca ches by an indi idual lee . We also an ou esul s
395
on o al alues, compa ing o example he Biomass Densi y BACI alue agg ega ed ac oss all
396
simula ed species o he ue e ec o he MPA on all ca ches ac oss all lee s (Figu e S31). Doing
397
so had no subs an ial impac on he co e esul s p esen ed in he body o he pape .398
24
MPAs gi en uncon olled o iola ions o he co e assump ions o hese indica o s such as di e -
488
ences in baseline habi a inside e sus ou side, ec ui men shocks, and ishing lee esponses, a e
489
common in ma ine social-ecological sys ems (Fe a o, Sanchi ico, and Smi h 2018; La sen, Meng,
490
and Kendall 2019; Hilbo n e al. 2022; O ando e al. 2021; Hop e al. 2024). Ou esul s show
491
ha e en when aced wi h hese biasing ac o s, inside-ou side indica o s s ill could be easonably
492
eliable p oxies o he ela i e conse a ion pe o mance o di e en MPAs, as e idenced by he
493
ela i ely high Spea man’s
𝜌
alues (Figu e 3). Howe e , hese inside-ou side indica o s we e
494
s ill gene ally imp ecise (high RMSE) and biased, indica ing ha one canno eliably in e p e he
495
speci ic alues o an inside-ou side indica o as he nume ical e ec on an MPA on conse a ion. 496
None o indica o s e alua ed in his s udy eliably acked he e ec s o MPAs on ishe y ca ches,
497
demons a ed h ough low and nega i e Spea man’s
𝜌
alues (all
|𝜌|
alues < 0.15), high RMSE,
498
and gene ally posi i e bias (Figu e 3). This highligh s an u gen need o esea ch on e ec i e
499
indica o s o he e ec s o MPAs on ishe y ca ches, as ou simula ion esul s indica e ha nega i e
500
e ec s can be common. Collec ion o ac ual da a on he economic pe o mance o ishe ies ( a he
501
han indi ec indica o s such as spillo e g adien s) could help p o ide be e es ima es o ca ch
502
e ec s, hough inding sui able “con ol” ishe ies o isola e he causal e ec o MPAs on o al
503
ca ch om o he exogenous shocks (e.g. changes in uel p ices o ma ke demand) will be di icul .
504
Nea – a indica o s, used as a measu e o spillo e e ec s, ailed o ack any MPA e ec s, despi e
505
s ong g adien s in simula ed biomass and e o being p oduced by ou model (Figu e S28). Bo h 506
simula ion modeling and empi ical e idence ha e con i med ha some le el o spillo e will
507
almos always occu a one o mo e li e s ages gi en he mo emen dynamics o ma ine o ganisms
508
(Cudney-Bueno e al. 2009; Di Lo enzo e al. 2020; F anceschini, Lynham, and Madin 2024;
509
Gaines e al. 2010; Hilbo n e al. 2004; Hilbo n e al. 2024). The mo e challenging ques ion
510
is wha does obse ing a spillo e g adien ell us? Ou esul s show ha unde ou simula ed
511
condi ions, he p esence o ue spillo e g adien s in o example biomass densi y o ishing e o
512
caused by an MPA, is no gene ally necessa y no su icien e idence o he e ec s o MPAs on
513
31
Biomass Inside,Biomass Ou side,To al Biomass, o Ca ch.514
Nea - a indica o s exploi ing g adien s ou side o MPAs me hods ha e a long his o y in MPA
515
science in pa because hey can be calcula ed using only pos -MPA ishe y-dependen da a, and do
516
no necessa ily equi e es ablishing moni o ing p og ams o ack condi ions p e-implemen a ion o
517
su ey wi hin MPAs (Halpe n, Les e , and Kellne 2009; Lynham and Villaseño -De bez 2024;
518
Medo , Lynham, and Rayno 2022; Robe s e al. 2001; Di Lo enzo, Claude , and Guide i 2016;
519
Me h a a 2020). While g adien me hods we e no e ec i e indica o s o any o he MPA e ec s
520
e alua ed in his s udy, esea ch is needed on whe he he e a e modi ica ions o hese designs ha
521
a e e ec i e o some use cases, gi en ha ishe y-dependen nea - a da a a e o en all ha a e
522
a ailable and as such a e likely o be used despi e any associa ed wa nings om wo ks such as his.
523
Fo example, pe haps condi ional on he li e his o y o he species g adien s a speci ic dis ances
524
a e mo e meaning ul han o he s. Ensembles o nea - a indica o s migh pe o m be e han any
525
indi idual one (Ande son e al. 2017).526
The e a e also many o he po en ial objec i es o MPAs besides hose e alua ed he e, which may
527
pe haps be eliably acked by g adien -based me hods. Fo example, inc eases in he size and
528
abundance o ish nea MPA bo de s may be o high alue o ec ea ional ishe s (F anceschini,
529
Lynham, and Madin 2024), e en i hose inc eases on he bo de do no necessa ily equa e o
530
commensu a e bene i s a he scale o he o al popula ion o ishe y.531
The poo pe o mance o nea - a indica o s as p oxies o b oade MPA e ec s in ou model
532
p esen s a majo challenge o e alua ing MPAs whe e only ishe y-dependen da a om ou side
533
MPAs a e a ailable. E alua ions o la ge high-seas MPAs c ea ed h ough e o s such as BBNJ
534
a e likely o depend solely on ishe ies da a om ou side he MPA (Medo , Lynham, and Rayno
535
2022; Lynham and Villaseño -De bez 2024), as he cos s o conduc ishe y-independen su eys in
536
la ge and emo e pelagic MPAs a e likely o be p ohibi i e. Hamp on e al. (2023) p o ides a good
537
example o in eg a ion o ishe y-dependen da a wi h p ocess-based models o es ima e he e ec s
538
o high-seas MPAs on una ishe ies. No el sou ces o da a such as he acous ic signals collec ed
539
32
by Fish Agg ega ing De ices used in una ishe ies could also be explo ed (Mo eno e al. 2019). 540
I is impo an o no e ha e en among he indica o s wi h ela i ely highe co ela ions (inside-
541
ou side), he sign o he co ela ion coe icien was no consis en . This means ha o example
542
when compa ing wo MPAs, he MPA wi h he highe Biomass Densi y Response Ra io may
543
ha e he ela i ely be e ou come o Biomass Inside and To al Biomass, bu he ela i ely wo se
544
ou come o Biomass Ou side and Ca ch. To illus a e, Biomass Densi y Response Ra ios obse ed
545
in a ge ed ish species a ound Cali o nia MPAs we e gene ally posi i e, o en subs an ially so
546
(Smi h e al. 2024). Ou simula ion esul s show ha while highe esponse a ios we e gene ally
547
p oduced by be e Biomass Inside ou comes, highe Biomass Densi y Response Ra ios we e
548
associa ed wi h ela i ely wo se Biomass Ou side and Ca ch e ec s (Figu e 4). 549
We concu wi h Hop e al. (2024) ha while pu ely empi ical me hods ha e alue, e o s o es i-
550
ma e he e ec s o MPAs should ideally be placed in he con ex o he complexi ies and dynamics
551
o ma ine social-ecological sys ems. Bayesian es ima ion echniques p o ide a na u al means o
552
encoding p io in o ma ion based on o he s udies o bio-economic heo y. Use o spa io- empo al
553
modeling o app op ia ely s anda dize spa ial obse a ions (J. T. Tho son 2019) so ha hey can
554
be passed o o in eg a ed in o spa ial s ock assessmen s (Pun 2019) could p o ide s a is ically
555
igo ous es ima es o changes in ishing mo ali y a es and biomass ela i e o e e ence poin s as
556
a unc ion o MPA implemen a ion, hough hese me hods a e highly da a- and expe ise-in ensi e.
557
Ca ea s 558
Ou esul s depend on he assump ions embedded in he ma lin ope a ing model (O ando e
559
al. 2023). While al e na e assump ions could be used, he co e o ou esul s a e a unc ion o
560
assump ions ha a e b oadly ep esen a i e o eal ishe ies: bo h ish and ishe s mo e, and
561
he e is o en spa ial and empo al he e ogenei y in li e his o y, mo emen , habi a use, and lee
562
dynamics. While we ha e aimed o p oduce plausible simula ed s a es (e.g. by cons aining he li e
563
his o y o he species o common a che ypes), we canno assign eal-wo ld likelihood o any o ou
564
33
simula ed s a es, and some s a es and hei associa ed MPA e ec s and indica o pe o mances may
565
be mo e ypical in eali y han o he s.566
The model also omi s ce ain ecological p ocesses, such as ophic in e ac ions o empo al ends
567
in habi a o li e his o y. These empo al ends a e likely o become inc easingly impo an o
568
accoun o gi en he e ec s o clima e change on global oceans (F eds on-He mann e al. 2020).
569
We also assessed a subse o possible MPA e ec s and hei in e ac ions, omi ing po en ially
570
impo an bu mo e challenging o model objec i es such as ishe y p o i abili y. This could ha e
571
impo an implica ions; o example in scena ios whe e MPAs dec ease ca ch, he economic
572
condi ion and ood secu i y o a communi y could s ill go up in o al i hose dec eases in ca ch
573
we e o se by he economic bene i s o ou ism oppo uni ies caused by an MPA (Ban e al. 2019).
574
In o de o calcula e ou empi ical indica o s, we assumed ha all da a a e obse ed wi hou e o .
575
Obse a ion e o and sample sizes will u he a ec how well indica o s ack MPA e ec s. All
576
simula ed da a we e collec ed a e 20 yea s o MPA p o ec ion. MPA indica o s and e ec s bo h
577
e ol e o e ime (Hop e al. 2024; O ando, Doughe y, and Wilson 2016) and so u u e s udies
578
could u he examine he ways in which di e en ime pe iods o sampling a ec esul s. We
579
assumed ha MPAs a e ully no- ake and we e pe ec ly en o ced; iola ions o ei he o hese
580
assump ions would u he complica e s udies o MPA e ec s (Claude and Guide i 2010).581
In gene al, eal empi ical e alua ions o MPA e ec s should conside good p ac ices in causal
582
in e ence in spa ial social-ecological sys ems, see Fe a o, Sanchi ico, and Smi h (2018), McEl-
583
ea h (2020), Zuu (2009), By nes and Dee (2025), Pun (2019), J. Tho son and K is ensen (2024),
584
T edennick e al. (2021), G ace (2024), Whi e e al. (2011), Hop e al. (2024), Hilbo n e al.
585
(2022), and O ando e al. (2021) o use ul insigh s in his space.586
Conclusions587
The coming decades a e likely o see a apid expansion in he use o MPAs o a ious o ms,
588
bo h in coas al seas and in dynamic open-ocean sys ems. The simula ion modeling in o med by
589
34
bio-economic heo y implemen ed he e suppo s he basic conclusion ha his expansion is likely
590
o p oduce conse a ion gains inside bo de s and a he o al popula ion le el, hough he exac
591
magni ude o hese bene i s is highly unce ain. Howe e , he e ec s o hese MPAs ou side hei
592
bo de s, on bo h biomass and ca ch, is a less ce ain and may be highly posi i e, highly nega i e,
593
o anywhe e in be ween, wi h nega i e ca ch e ec s being mo e common in ou simula ions. 594
As such, i is impo an ha we be able o measu e he e ec s o MPAs on a ange o objec i es,
595
so ha we can quan i y he cos s and bene i s p oduced by MPAs and subsequen ly adap ou
596
unde s anding o e ec i e design and use o MPAs as needed. Ou esul s show ha while impe -
597
ec , some empi ical indica o s, pa icula ly inside-ou side indica o s such as esponse a ios, can
598
be eliable indica o s o conse a ion ou comes o MPAs, pa icula ly inside hei bo de s. Bu
599
ha ew empi ical indica o s commonly in use eliably ack he e ec s o MPAs ou side hei
600
bo de s, pa icula ly on ishe y ca ches. G adien based nea - a me hods (e.g. hose ha assess
601
spillo e based on biomass densi ies nea ela i e o a om MPA bo de s), while commonly used,
602
pe o med pa icula ly poo ly as an indica o o any MPA e ec e alua ed in his pape . 603
The nex gene a ion o MPA esea ch u gen ly needs collabo a ion ac oss disciplines such as
604
conse a ion biology, ecology, ishe ies, and social sciences o de elop su icien ly eliable and
605
p ac ical me hods o acking he e ec s o MPAs. Doing so will help ensu e ha u u e expansion
606
o p o ec ed a eas in he wo ld’s oceans s and he bes chance o achie ing posi i e and equi able
607
ou comes o na u e and people. 608
Acknowledgmen s 609
This pape was g ea ly aided by discussions wi h pa icipan s o he A ea-Based Managemen
610
wo king g oup o he “Helping science ad ance policy in ocean conse a ion” wo kshop con ened
611
by he The Cen e o Sus aining Sea ood a he Uni e si y o Washing on, Ma ch 2-3 2024. 612
35
Au ho Con ibu ions613
DO and CL concei ed o he s udy and w o e he manusc ip . All analyses pe o med by DO.614
Con lic o In e es S a emen 615
The au ho s decla e no con lic s o in e es .616
Da a A ailabili y S a emen 617
Open Resea ch s a emen : No da a we e collec ed o his s udy. This submission uses no el
618
code, which is p o ided, pe ou equi emen s, in an ex e nal eposi o y o be e alua ed du ing he
619
pee e iew p ocess. The link o his ex e nal eposi o y is h ps://gi hub.com/DanO ando/mpa-
620
e ec s-and-indica o s A p e-p in a chi e o he code along wi h all esul s needed o ep oduce
621
his pape is also a ailable a h ps://doi.o g/10.5281/zenodo.17308920. This zenodo eposi o y
622
will be upg aded o a pe manen a chi e o he inal esul s and pape upon publica ion.623
36
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