# Drifting fish aggregation devices as a tool to study oceanic marine protected areas

Arthur Blanluet, Edward T Game, Kydd Pollock, Nicholas H Wolff, Jason D

Everett, Sandra Neubert, Daniel C Dunn, Anthony J Richardson

► To cite this version:

Arthur Blanluet, Edward T Game, Kydd Pollock, Nicholas H Wolff, Jason D Everett, et al. Drifting fish aggregation devices as a tool to study oceanic marine protected areas. 2024. hal-04548790

HAL Id: hal-04548790

https://hal.science/hal-04548790

Preprint submitted on 16 Apr 2024

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Drifting fish aggregation devices as a tool to study oceanic marine protected areas

Arthur Blanluet, Edward T Game, Kydd Pollock, Nicholas H Wolff, Jason D

Everett, Sandra Neubert, Daniel C Dunn, Anthony J Richardson

► To cite this version:

Arthur Blanluet, Edward T Game, Kydd Pollock, Nicholas H Wolff, Jason D Everett, et al. Drifting fish aggregation devices as a tool to study oceanic marine protected areas. 2024. hal-04548790

HAL Id: hal-04548790

https://hal.science/hal-04548790v1

Preprint submitted on 16 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Drifting fish aggregation devices as a tool to study oceanic marine protected areas

Arthur Blanлюet¹,²*, Edward T. Game², Kydd Pollock³, Nicholas H. Wolff⁴, Jason D. Everett¹,⁵,
Sandra Neubert¹,⁶,⁷,⁸, Daniel C. Dunn¹,⁶, Anthony J. Richardson¹,⁶,⁷

¹School of the Environment, The University of Queensland, St Lucia, QLD, 4067, Australia

²The Nature Conservancy, South Brisbane, Queensland 4101, Australia

³The Nature Conservancy, Honolulu, HI 96817, USA

⁴The Nature Conservancy, Brunswick, ME 04011, USA

⁵Centre for Marine Science and Innovation, University of New South Wales, Sydney, NSW,
Australia

⁶Centre for Biodiversity and Conservation Science (CBCS), The University of Queensland, St Lucia, 4072, QLD, Australia

⁷Commonwealth Scientific and Industrial Research Organization (CSIRO) Environment, Queensland Biosciences Precinct (QBP), St Lucia, QLD, 4067, Australia

⁸Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall, TR10 9FE, UK

*Corresponding author: arthur.blanлюet0@gmail.com

## 1 Abstract

To meet global conservation targets, there is a growing effort to establish oceanic (waters

> 200 m depth) marine protected areas (oMPAs). However, despite substantial evidence for

benefits of coastal MPAs to fish and fisheries, the effectiveness of oMPAs has been

challenging to assess robustly, mainly because targeted data collection is expensive so

research often relies on catch data restricted to areas outside oMPA boundaries. Here we

explore the use of drifting fish aggregating devices (dFADs) as a novel method to assess the

effectiveness of oMPAs. We used acoustic data from 902 dFADs deployed by the fishing

industry that drifted across the US Pacific Remote Island Marine National Monument around

Palmyra Atoll – providing data both inside and outside the oMPA – to study spatial variation

in tuna biomass. Using a generalised additive mixed model with a suite of environmental

covariates, we found the relationship between tuna biomass and many environmental

covariates made intuitive ecological sense with respect to known tuna behaviour, providing

confidence in the model. We found no measurable increase in tuna biomass inside the oMPA.
# Drifting fish aggregation devices as a tool to study oceanic marine protected areas

Arthur Blanлюet¹,²*, Edward T. Game², Kydd Pollock³, Nicholas H. Wolff⁴, Jason D. Everett¹,⁵,
Sandra Neubert¹,⁶,⁷,⁸, Daniel C. Dunn¹,⁶, Anthony J. Richardson¹,⁶,⁷

¹School of the Environment, The University of Queensland, St Lucia, QLD, 4067, Australia

²The Nature Conservancy, South Brisbane, Queensland 4101, Australia

³The Nature Conservancy, Honolulu, HI 96817, USA

⁴The Nature Conservancy, Brunswick, ME 04011, USA

⁵Centre for Marine Science and Innovation, University of New South Wales, Sydney, NSW,
Australia

⁶Centre for Biodiversity and Conservation Science (CBCS), The University of Queensland, St Lucia, 4072, QLD, Australia

⁷Commonwealth Scientific and Industrial Research Organization (CSIRO) Environment, Queensland Biosciences Precinct (QBP), St Lucia, QLD, 4067, Australia

⁸Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall, TR10 9FE, UK

*Corresponding author: arthur.blanлюet0@gmail.com

## 1 Abstract

To meet global conservation targets, there is a growing effort to establish oceanic (waters

> 200 m depth) marine protected areas (oMPAs). However, despite substantial evidence for

benefits of coastal MPAs to fish and fisheries, the effectiveness of oMPAs has been

challenging to assess robustly, mainly because targeted data collection is expensive so

research often relies on catch data restricted to areas outside oMPA boundaries. Here we

explore the use of drifting fish aggregating devices (dFADs) as a novel method to assess the

effectiveness of oMPAs. We used acoustic data from 902 dFADs deployed by the fishing

industry that drifted across the US Pacific Remote Island Marine National Monument around

Palmyra Atoll – providing data both inside and outside the oMPA – to study spatial variation

in tuna biomass. Using a generalised additive mixed model with a suite of environmental

covariates, we found the relationship between tuna biomass and many environmental

covariates made intuitive ecological sense with respect to known tuna behaviour, providing

confidence in the model. We found no measurable increase in tuna biomass inside the oMPA.
This finding was likely influenced by the low fishing pressure around this particular oMPA, and regions with greater contrast in fishing pressure might show different results. This research highlights the utility of dFADs as a cost-effective tool for future studies to assess fish biomass in oMPAs, especially in regions difficult or costly to sample.

**Keywords:** tropical tunas, echosounder buoys, acoustics, marine reserves, Palmyra Atoll

## Introduction

Marine protected areas (MPAs) are recognized as a powerful conservation tool for biodiversity (1). Increasingly, oceanic MPAs (oMPAs), those beyond the continental shelf >200 m depth (2), are being declared to meet objectives of international conservation agreements, including the Kunming-Montreal Global Biodiversity Framework target 3, to protect 30% of marine areas (https://www.cbd.int/gbf/). To ensure adequate representation of Earth's ecosystems in protected areas, more oceanic habitats will need to be protected (2). As most oceanic habitats are in the high seas, there is now further opportunity for conservation of these areas with the recent agreement of the UN High Seas Treaty (https://www.un.org/bbnj), which lays the groundwork for a global mechanism to establish MPAs in waters beyond national jurisdiction.

Although oceanic marine protected areas (oMPAs) aim for broad objectives, including pelagic and benthic biodiversity conservation and potential fisheries benefits, most scientific studies primarily emphasize fisheries benefits (2, 3). While the conservation benefits of coastal MPAs for exploited populations are clear (4–6), the evidence for the benefits of oMPAs is equivocal. The high mobility of pelagic fauna may pose a challenge for area-based management (7), and direct evaluations of the benefits of oMPAs have been difficult because of the high-cost of fisheries-independent sampling and thus a reliance on fisheries data that do not provide information within the boundaries of the oMPAs after they are established.

To assess the potential conservation benefits of oMPAs, most studies have looked at tuna because they are the main exploited species in oceanic environments (8). Tuna fisheries have mainly used longlines and purse seines over recent decades. Over the past 20 years, purse seine fleets have largely moved from free-school tracking, using sonars and natural markers such as birds or dolphins to find tuna aggregations, to deploying drifting fish aggregating devices (dFADs) to attract and retain tuna (9). These artificial platforms (Fig. 1B) comprise a simple raft equipped with a basic echosounder, a GPS device, and a long tail of net or rope to attract tuna and slow the drift of the dFAD. dFADs are now ubiquitous in the tropical and equatorial zone of the Atlantic, Pacific and Indian Oceans. In the Western Pacific alone, between 40,000 and 60,000 dFADs are deployed each year (L. Escalle pers. comm.).

Although tuna fishing fleets have substantially benefited from this technological leap (10), conservation and fisheries science could also potentially benefit from this network of drifting platforms (11). For example, dFADs can be used to study tuna aggregation behaviour (12) or build fisheries-independent indicators for stock assessment (13). Notably, dFADs cross economic exclusive zones (EEZ) and MPA boundaries alike, making them a promising tool to sample tuna both inside and outside oMPAs. The lack of data within oMPAs because of the reliance on fisheries catch data has been a blind spot in assessing their ecological benefits (14) that dFADs could address.
This finding was likely influenced by the low fishing pressure around this particular oMPA, and regions with greater contrast in fishing pressure might show different results. This research highlights the utility of dFADs as a cost-effective tool for future studies to assess fish biomass in oMPAs, especially in regions difficult or costly to sample.

**Keywords:** tropical tunas, echosounder buoys, acoustics, marine reserves, Palmyra Atoll

## Introduction

Marine protected areas (MPAs) are recognized as a powerful conservation tool for biodiversity (1). Increasingly, oceanic MPAs (oMPAs), those beyond the continental shelf >200 m depth (2), are being declared to meet objectives of international conservation agreements, including the Kunming-Montreal Global Biodiversity Framework target 3, to protect 30% of marine areas (https://www.cbd.int/gbf/). To ensure adequate representation of Earth's ecosystems in protected areas, more oceanic habitats will need to be protected (2). As most oceanic habitats are in the high seas, there is now further opportunity for conservation of these areas with the recent agreement of the UN High Seas Treaty (https://www.un.org/bbnj), which lays the groundwork for a global mechanism to establish MPAs in waters beyond national jurisdiction.

Although oceanic marine protected areas (oMPAs) aim for broad objectives, including pelagic and benthic biodiversity conservation and potential fisheries benefits, most scientific studies primarily emphasize fisheries benefits (2, 3). While the conservation benefits of coastal MPAs for exploited populations are clear (4–6), the evidence for the benefits of oMPAs is equivocal. The high mobility of pelagic fauna may pose a challenge for area-based management (7), and direct evaluations of the benefits of oMPAs have been difficult because of the high-cost of fisheries-independent sampling and thus a reliance on fisheries data that do not provide information within the boundaries of the oMPAs after they are established.

To assess the potential conservation benefits of oMPAs, most studies have looked at tuna because they are the main exploited species in oceanic environments (8). Tuna fisheries have mainly used longlines and purse seines over recent decades. Over the past 20 years, purse seine fleets have largely moved from free-school tracking, using sonars and natural markers such as birds or dolphins to find tuna aggregations, to deploying drifting fish aggregating devices (dFADs) to attract and retain tuna (9). These artificial platforms (Fig. 1B) comprise a simple raft equipped with a basic echosounder, a GPS device, and a long tail of net or rope to attract tuna and slow the drift of the dFAD. dFADs are now ubiquitous in the tropical and equatorial zone of the Atlantic, Pacific and Indian Oceans. In the Western Pacific alone, between 40,000 and 60,000 dFADs are deployed each year (L. Escalle pers. comm.).

Although tuna fishing fleets have substantially benefited from this technological leap (10), conservation and fisheries science could also potentially benefit from this network of drifting platforms (11). For example, dFADs can be used to study tuna aggregation behaviour (12) or build fisheries-independent indicators for stock assessment (13). Notably, dFADs cross economic exclusive zones (EEZ) and MPA boundaries alike, making them a promising tool to sample tuna both inside and outside oMPAs. The lack of data within oMPAs because of the reliance on fisheries catch data has been a blind spot in assessing their ecological benefits (14) that dFADs could address.
Here, we assessed the potential of using dFADs as a sampling platform to investigate tuna biomass differences inside and outside of an oMPA. We used data from dFADs collected in the vicinity of the protected island of Palmyra Atoll (part of the US "Pacific Remote Island Marine National Monument", PRIMNM; Fig. 1A, C). The objectives of the PRIMNM are diverse, from the protection of habitats, ecosystems and biodiversity, to the minimization of human impacts and the support of exploited fish populations (15, 2). PRIMNM exemplifies the typical traits of most oMPAs in terms of its remoteness, relatively large size (km²), and inclusion of habitats from inshore around an island to depths > 4000 m. We used data from 902 dFADs to explore whether tuna biomass inside the unfished oMPA was higher than in waters surrounding the MPA which were open to tuna fishing. Data were obtained through the "FAD Watch Program", an agreement between The Nature Conservancy (TNC) and several purse seine fishing companies to mitigate dFAD groundings on Palmyra's sensitive coral reef ecosystem.
Here, we assessed the potential of using dFADs as a sampling platform to investigate tuna biomass differences inside and outside of an oMPA. We used data from dFADs collected in the vicinity of the protected island of Palmyra Atoll (part of the US "Pacific Remote Island Marine National Monument", PRIMNM; Fig. 1A, C). The objectives of the PRIMNM are diverse, from the protection of habitats, ecosystems and biodiversity, to the minimization of human impacts and the support of exploited fish populations (15, 2). PRIMNM exemplifies the typical traits of most oMPAs in terms of its remoteness, relatively large size (km²), and inclusion of habitats from inshore around an island to depths > 4000 m. We used data from 902 dFADs to explore whether tuna biomass inside the unfished oMPA was higher than in waters surrounding the MPA which were open to tuna fishing. Data were obtained through the "FAD Watch Program", an agreement between The Nature Conservancy (TNC) and several purse seine fishing companies to mitigate dFAD groundings on Palmyra's sensitive coral reef ecosystem.
69

# Results

70 From August 2021 to July 2023, a total of 902 Satlink dFADs, belonging to the collaborating
71 fishing companies, crossed the sampling area boundaries (Fig. 1C). A preliminary analysis of
72 the tuna biomass inside and outside PRIMNM based on inverse distance weighted smoothing
73 suggested that there was no increase in tuna biomass in the oMPA compared with
74 immediately adjacent waters (Fig. 2). Tuna biomass varied little across the region (from 3.25
75 to 3.5 t on average), but was highest in the South and lowest in the North, with little observable
76 impact of Palmyra Atoll or the oMPA. However, these data were collected across different
77 temporal (e.g., years, seasons, time of day) and environmental conditions (sea surface
78 temperature and chlorophyll), and from different dFADs, which may have been assembled
79 differently, all of which could influence the results.

80 We used a generalised additive mixed model (GAMM) to isolate the potential effect of the
81 oMPA on tuna biomass whilst adjusting for the effect of a suite of temporal and environmental
82 variables on tuna biomass. The response was 2-hourly tuna biomass data from the dFADs.
83 The predictors include a fixed effect for the oMPA with two levels (Inside or Outside),
84 environmental covariates as fixed effects, and a random effect for individual dFADs to account
85 for differences in each dFAD in terms of their physical and bioacoustics configuration and
86 calibration.

87 When adjusting for temporal and environmental covariates and the random effect for dFADs,
88 the GAMM showed no significant difference in tuna biomass inside and outside the oMPA.
89 The mean biomass (±standard deviation) inside the oMPA (0.100 ± 0.013 t) was similar to that
90 outside (0.102 ± 0.013 t) (Fig. 3a), suggesting no observable increase in tuna biomass in the
91 oMPA.

92 The most significant predictor of tuna biomass in the GAMM was the interaction between the
93 Depth layer and Time of day. We found tuna biomass around dFADs to be significantly driven
94 by the day/night cycle (Fig. 3d), with tuna biomass associated with dFADs peaking at sunrise
95 (5 am), declining during the day, and lowest just before nightfall (~4 pm). The greatest
96 amplitude in the daily cycle was deeper in the water column, and there was a strong decrease
97 in the amplitude at shallower depths, with the surface layer exhibiting little change. There was
98 also a slight shift in the biomass peak time for shallower depths: deeper layers peak around 5
99 am, whereas mid layers (from 36.6 m to 81.4 m) peaked between 3 am and 5 am.

100 Other temporal predictors in the GAMM were less important. Tuna biomass over time (Fig. 3e)
101 is marked by a peak in the first few days each dFAD entered the study region, followed by a
102 decrease over time. Our sampled areas did not represent the full trajectory of the dFADs and
103 the lack of data for a longer period before the MPA boundary makes the interpretation of this
104 variable difficult. However, the overall decrease of biomass with time suggests that dFADs did
105 not "drain" tuna from the MPA. Tuna biomass varied significantly seasonally (Fig. 3c), with a
106 peak during summer (July-September) and a low during winter (January-March), with spring
107 and autumn having intermediate biomass. There is also a significant difference in tuna
108 biomass between the two years of sampling (Fig. 3b), with greater biomass in 2021-2022 than
109 in 2022-2023.
69

# Results

70 From August 2021 to July 2023, a total of 902 Satlink dFADs, belonging to the collaborating
71 fishing companies, crossed the sampling area boundaries (Fig. 1C). A preliminary analysis of
72 the tuna biomass inside and outside PRIMNM based on inverse distance weighted smoothing
73 suggested that there was no increase in tuna biomass in the oMPA compared with
74 immediately adjacent waters (Fig. 2). Tuna biomass varied little across the region (from 3.25
75 to 3.5 t on average), but was highest in the South and lowest in the North, with little observable
76 impact of Palmyra Atoll or the oMPA. However, these data were collected across different
77 temporal (e.g., years, seasons, time of day) and environmental conditions (sea surface
78 temperature and chlorophyll), and from different dFADs, which may have been assembled
79 differently, all of which could influence the results.

80 We used a generalised additive mixed model (GAMM) to isolate the potential effect of the
81 oMPA on tuna biomass whilst adjusting for the effect of a suite of temporal and environmental
82 variables on tuna biomass. The response was 2-hourly tuna biomass data from the dFADs.
83 The predictors include a fixed effect for the oMPA with two levels (Inside or Outside),
84 environmental covariates as fixed effects, and a random effect for individual dFADs to account
85 for differences in each dFAD in terms of their physical and bioacoustics configuration and
86 calibration.

87 When adjusting for temporal and environmental covariates and the random effect for dFADs,
88 the GAMM showed no significant difference in tuna biomass inside and outside the oMPA.
89 The mean biomass (±standard deviation) inside the oMPA (0.100 ± 0.013 t) was similar to that
90 outside (0.102 ± 0.013 t) (Fig. 3a), suggesting no observable increase in tuna biomass in the
91 oMPA.

92 The most significant predictor of tuna biomass in the GAMM was the interaction between the
93 Depth layer and Time of day. We found tuna biomass around dFADs to be significantly driven
94 by the day/night cycle (Fig. 3d), with tuna biomass associated with dFADs peaking at sunrise
95 (5 am), declining during the day, and lowest just before nightfall (~4 pm). The greatest
96 amplitude in the daily cycle was deeper in the water column, and there was a strong decrease
97 in the amplitude at shallower depths, with the surface layer exhibiting little change. There was
98 also a slight shift in the biomass peak time for shallower depths: deeper layers peak around 5
99 am, whereas mid layers (from 36.6 m to 81.4 m) peaked between 3 am and 5 am.

100 Other temporal predictors in the GAMM were less important. Tuna biomass over time (Fig. 3e)
101 is marked by a peak in the first few days each dFAD entered the study region, followed by a
102 decrease over time. Our sampled areas did not represent the full trajectory of the dFADs and
103 the lack of data for a longer period before the MPA boundary makes the interpretation of this
104 variable difficult. However, the overall decrease of biomass with time suggests that dFADs did
105 not "drain" tuna from the MPA. Tuna biomass varied significantly seasonally (Fig. 3c), with a
106 peak during summer (July-September) and a low during winter (January-March), with spring
107 and autumn having intermediate biomass. There is also a significant difference in tuna
108 biomass between the two years of sampling (Fig. 3b), with greater biomass in 2021-2022 than
109 in 2022-2023.
There were several important environmental predictors in the GAMM. Tuna biomass was significantly related to Current velocity (Fig. 3h), with higher biomass with faster currents >0.5 m s⁻¹. Tuna biomass was also related to Thermocline depth (Fig. 3i), with a deeper thermocline associated with higher tuna biomass. Tuna biomass was significantly, positively and near-linearly related to Chlorophyll (Fig. 3j), Moon illumination (Fig. 3f) and Salinity (Fig. 3i), but there was little effect of Sea surface temperature. Finally, there was a proximity effect among dFADs, with lower tuna biomass when Distance to nearest dFADs was <50 km apart (Fig. 3g).
There were several important environmental predictors in the GAMM. Tuna biomass was significantly related to Current velocity (Fig. 3h), with higher biomass with faster currents >0.5 m s⁻¹. Tuna biomass was also related to Thermocline depth (Fig. 3i), with a deeper thermocline associated with higher tuna biomass. Tuna biomass was significantly, positively and near-linearly related to Chlorophyll (Fig. 3j), Moon illumination (Fig. 3f) and Salinity (Fig. 3i), but there was little effect of Sea surface temperature. Finally, there was a proximity effect among dFADs, with lower tuna biomass when Distance to nearest dFADs was <50 km apart (Fig. 3g).
117 Discussion

118 To test whether the dFAD acoustic data of tuna biomass could be used to assess the potential
efficacy of oMPAs, we developed a GAMM to estimate biomass in the PRIMNM marine
reserve and the surrounding waters within the United States exclusive economic zone. While
we found no observable effect of the oMPA on tuna biomass, the interpretability of many of
the covariates in the GAMM suggests that the bioacoustic data from the dFADs reflect real
trends in tuna biomass and therefore was sufficiently robust to observe changes in tuna
biomass inside and outside the oMPA. This study is the first to investigate the biomass of tuna
inside and outside an oMPA using bioacoustic data from dFADs, and will hopefully lead to
similar studies of other oMPAs in the future.
126

127 No visible effect of the MPA on tuna biomass

128 There have been contradictory results from studies investigating the effect of oMPAs on tuna
biomass, with some studies reporting tuna spillover (16, 17) and others not (18–20), although
all these studies used fisheries catch data. Tuna are highly mobile and migratory, and previous
work has called into question the utility of oMPAs given the relatively small size of oMPAs
(~230 km side squares for the PRIMNM that surround Palmyra) compared to the potential
daily range of tuna species associated with dFADs (skpijack: ~80 km.d⁻¹; yellow fin: ~110-135
km.d⁻¹, big eye tuna : ~110 km.d⁻¹; based on their mean swimming speed and directed
movement, Shadwick et al., 2013). Others have questioned the utility of MPAs in general due
to the displacement (not reduction) of fishing effort (22). Lack of evidence of differences in
biomass inside and outside of oMPAs could also be a consequence of low fishing effort in the
vicinity of many oMPAs before and after their closing (14).
138

139 Given low levels of fishing effort in the US EEZ around PRIMNM (see Supplementary Material
S2), we might not expect to see significant differences in biomass inside and outside of
PRIMNM. This was borne out through our analysis, which shows no evidence of tuna spillover
from Palmyra and Kingman PRIMNM in the immediate vicinity of the oMPA, consistent with
study of the same ecosystem based on Catch Per Unit Effort (CPUE) data (19) and satellite
positions of fishing vessels (14). Although we might expect a greater potential effect of an
oMPA in heavily fished areas (23), low levels of fishing effort are common in many oceanic
areas (14), so the study here is not atypical of the case for other oMPAs. Increasing target
tuna populations is not an objective of the marine reserve, nor is the marine reserve placed to
provide protection to a critical life history stage of tuna (e.g., a spawning aggregation), both of
which might influence the biomass seen inside vs outside the protected area. However, as
was seen with the levels of fishing effort, this is not abnormal: most oMPAs are not sited or
designated with the purpose of conserving pelagic populations (2).
145
146
147
148
149
150
151

152 The present study also showed that tuna biomass around dFADs decreased slowly with time
(based on the Time predictor, Fig. 3e). If there was more tuna inside than outside the oMPA,
we might expect dFADs to aggregate more tuna as they crossed the MPA boundary and an
increase in tuna biomass over time whilst in the MPA. This suggests that there was no increase
in tuna biomass after dFADs entered the MPA, and is consistent with the findings from the
MPA predictor in the GAMM. However, clear interpretation of the Time predictor is challenging
because we did not know the last time each dFAD was fished. This information was not readily
available but could be key in future studies.
158
159
117 Discussion

118 To test whether the dFAD acoustic data of tuna biomass could be used to assess the potential
efficacy of oMPAs, we developed a GAMM to estimate biomass in the PRIMNM marine
reserve and the surrounding waters within the United States exclusive economic zone. While
we found no observable effect of the oMPA on tuna biomass, the interpretability of many of
the covariates in the GAMM suggests that the bioacoustic data from the dFADs reflect real
trends in tuna biomass and therefore was sufficiently robust to observe changes in tuna
biomass inside and outside the oMPA. This study is the first to investigate the biomass of tuna
inside and outside an oMPA using bioacoustic data from dFADs, and will hopefully lead to
similar studies of other oMPAs in the future.
126

127 No visible effect of the MPA on tuna biomass

128 There have been contradictory results from studies investigating the effect of oMPAs on tuna
biomass, with some studies reporting tuna spillover (16, 17) and others not (18–20), although
all these studies used fisheries catch data. Tuna are highly mobile and migratory, and previous
work has called into question the utility of oMPAs given the relatively small size of oMPAs
(~230 km side squares for the PRIMNM that surround Palmyra) compared to the potential
daily range of tuna species associated with dFADs (skpijack: ~80 km.d⁻¹; yellow fin: ~110-135
km.d⁻¹, big eye tuna : ~110 km.d⁻¹; based on their mean swimming speed and directed
movement, Shadwick et al., 2013). Others have questioned the utility of MPAs in general due
to the displacement (not reduction) of fishing effort (22). Lack of evidence of differences in
biomass inside and outside of oMPAs could also be a consequence of low fishing effort in the
vicinity of many oMPAs before and after their closing (14).
138

139 Given low levels of fishing effort in the US EEZ around PRIMNM (see Supplementary Material
S2), we might not expect to see significant differences in biomass inside and outside of
PRIMNM. This was borne out through our analysis, which shows no evidence of tuna spillover
from Palmyra and Kingman PRIMNM in the immediate vicinity of the oMPA, consistent with
study of the same ecosystem based on Catch Per Unit Effort (CPUE) data (19) and satellite
positions of fishing vessels (14). Although we might expect a greater potential effect of an
oMPA in heavily fished areas (23), low levels of fishing effort are common in many oceanic
areas (14), so the study here is not atypical of the case for other oMPAs. Increasing target
tuna populations is not an objective of the marine reserve, nor is the marine reserve placed to
provide protection to a critical life history stage of tuna (e.g., a spawning aggregation), both of
which might influence the biomass seen inside vs outside the protected area. However, as
was seen with the levels of fishing effort, this is not abnormal: most oMPAs are not sited or
designated with the purpose of conserving pelagic populations (2).
145
146
147
148
149
150
151

152 The present study also showed that tuna biomass around dFADs decreased slowly with time
(based on the Time predictor, Fig. 3e). If there was more tuna inside than outside the oMPA,
we might expect dFADs to aggregate more tuna as they crossed the MPA boundary and an
increase in tuna biomass over time whilst in the MPA. This suggests that there was no increase
in tuna biomass after dFADs entered the MPA, and is consistent with the findings from the
MPA predictor in the GAMM. However, clear interpretation of the Time predictor is challenging
because we did not know the last time each dFAD was fished. This information was not readily
available but could be key in future studies.
158
159
161 There is compelling evidence that the bioacoustic data in the current study was sufficiently
162 informative to understand drivers of tuna biomass, and thus detect changes in tuna biomass
163 inside the oMPA. The available bioacoustic data were sufficiently sensitive to detect
164 relationships between tuna biomass and various predictors in the GAMM that that were
165 consistent with known tuna ecology. For example, tuna biomass associated with dFADs
166 peaked at sunrise (5 am, Fig. 3d), consistent with other work that shows tuna at that time form
167 fairly coherent schools in close proximity to dFADs (24). During most daylight hours, we found
168 that tuna stay in deeper layers around the dFAD and above the thermocline, as found by
169 Matsumoto et al. (2016). During the early evening we noted that tuna biomass around the
170 dFADs declined, likely because tuna disperse from the vicinity of the dFADs for opportunistic
171 foraging on the migrating deep scattering layer (26).

172 We also found that tuna biomass was concentrated in deeper layers, suggesting that
173 populations in the area comprise larger tuna species (27). Generally, larger tuna species
174 gather in deeper layers around dFADs, whereas smaller tuna species aggregate in shallow
175 layers (21, 28). However, this separation of species/size by depth is less clear in the Central
176 Pacific Ocean (25).

177 Consistent with the known longitudinal seasonal migration of skipjack tunas, we found greatest
178 tuna biomass in summer and autumn, as the centre of skipjack biomass in the central Pacific
179 Ocean is generally located further East in winter (~140°E) and further west in summer (29),
180 closer to our sampling area (~160°W). This seasonal skipjack migration is strongly influenced
181 by the El Niño Southern Oscillation, with a shift to the East during El Niños, and a shift to the
182 West during La Niña (29). Large-scale shifts in tuna distribution in response to the El Niño
183 Southern Oscillation could explain the difference in tuna biomass between our two sampling
184 years. Our relatively high tuna biomass records in 2021-2022 coincided with a strong La Niña<fnref n="1" />
185 (30), suggesting a shift of skipjack biomass towards the western Pacific Ocean, potentially
186 increasing the tuna biomass off Palmyra Atoll. By contrast, the lower tuna biomass observed
187 in 2022-2023 occurred during a neutral period when skipjack biomass is likely highest towards
188 the eastern Pacific and away from our study area.

189 Most environmental predictors in the GAMM were relatively weak but several were
190 interpretable. We found increased tuna biomass around dFADs at full moon (higher light
191 levels) and lower biomass at new moon (Fig. 3f). This is consistent with data on bigeye tuna,
192 which have a stronger hunting behaviour at new moon than at full moon (31), presumably
193 foraging in low light and aggregating around dFADs in better light conditions. There appeared
194 to be greater tuna biomass when there was more chlorophyll (Fig. 3j), which could be an index
195 of greater prey availability (32). Interestingly, we found that the distance to the nearest dFAD
196 shows that tuna biomass increased as the distance between dFADs increased from 0 to 50
197 km (Fig. 3g). This suggests that tuna may split their biomass among available dFADs in the
198 vicinity (31, 33), with no difference when the distance between two dFADs is >50 km.

<footnote n="1">¹ https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php</footnote>
160 The robustness of using dFADs for understanding drivers of tuna biomass

161 There is compelling evidence that the bioacoustic data in the current study was sufficiently
162 informative to understand drivers of tuna biomass, and thus detect changes in tuna biomass
163 inside the oMPA. The available bioacoustic data were sufficiently sensitive to detect
164 relationships between tuna biomass and various predictors in the GAMM that that were
165 consistent with known tuna ecology. For example, tuna biomass associated with dFADs
166 peaked at sunrise (5 am, Fig. 3d), consistent with other work that shows tuna at that time form
167 fairly coherent schools in close proximity to dFADs (24). During most daylight hours, we found
168 that tuna stay in deeper layers around the dFAD and above the thermocline, as found by
169 Matsumoto et al. (2016). During the early evening we noted that tuna biomass around the
170 dFADs declined, likely because tuna disperse from the vicinity of the dFADs for opportunistic
171 foraging on the migrating deep scattering layer (26).

172 We also found that tuna biomass was concentrated in deeper layers, suggesting that
173 populations in the area comprise larger tuna species (27). Generally, larger tuna species
174 gather in deeper layers around dFADs, whereas smaller tuna species aggregate in shallow
175 layers (21, 28). However, this separation of species/size by depth is less clear in the Central
176 Pacific Ocean (25).

177 Consistent with the known longitudinal seasonal migration of skipjack tunas, we found greatest
178 tuna biomass in summer and autumn, as the centre of skipjack biomass in the central Pacific
179 Ocean is generally located further East in winter (~140°E) and further west in summer (29),
180 closer to our sampling area (~160°W). This seasonal skipjack migration is strongly influenced
181 by the El Niño Southern Oscillation, with a shift to the East during El Niños, and a shift to the
182 West during La Niña (29). Large-scale shifts in tuna distribution in response to the El Niño
183 Southern Oscillation could explain the difference in tuna biomass between our two sampling
184 years. Our relatively high tuna biomass records in 2021-2022 coincided with a strong La Niña<fnref n="1" />
185 (30), suggesting a shift of skipjack biomass towards the western Pacific Ocean, potentially
186 increasing the tuna biomass off Palmyra Atoll. By contrast, the lower tuna biomass observed
187 in 2022-2023 occurred during a neutral period when skipjack biomass is likely highest towards
188 the eastern Pacific and away from our study area.

189 Most environmental predictors in the GAMM were relatively weak but several were
190 interpretable. We found increased tuna biomass around dFADs at full moon (higher light
191 levels) and lower biomass at new moon (Fig. 3f). This is consistent with data on bigeye tuna,
192 which have a stronger hunting behaviour at new moon than at full moon (31), presumably
193 foraging in low light and aggregating around dFADs in better light conditions. There appeared
194 to be greater tuna biomass when there was more chlorophyll (Fig. 3j), which could be an index
195 of greater prey availability (32). Interestingly, we found that the distance to the nearest dFAD
196 shows that tuna biomass increased as the distance between dFADs increased from 0 to 50
197 km (Fig. 3g). This suggests that tuna may split their biomass among available dFADs in the
198 vicinity (31, 33), with no difference when the distance between two dFADs is >50 km.

<footnote n="1">¹ https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php</footnote>
199 **Caveats**

200 Our study has several caveats. First, biomass values at depth must be interpreted with some
201 caution as they could be overestimated. Tuna biomass is calculated using Satlink software
202 based on the target strength of 5 kg skipjack tuna, a species without a swimbladder. Thus,
203 fish with swimbladders such as yellowfin and bigeye tuna that are likely to be present deeper
204 in the water column could inflate the total tuna biomass estimates. Second, echosounders on
205 dFADs are configured to estimate aggregating tuna, which could exhibit different dynamics
206 than free-schooling tuna (34). Further, dFADs are designed to sample tuna, so other parts of
207 the ecosystem – including other fish species abundances – remain unknown. Third, each
208 observation by a dFAD is not truly independent of other observations from the same dFAD,
209 as tuna are following the platform and each dFAD is simultaneously an observing and
210 aggregating device. However, including the random effect for dFAD in the GAMM and the
211 large number of dFADs in the dataset (n=902) minimises this limitation.

212 **dFADs as a scientific tool**

213 The interpretability of many of the predictors in the model suggest dFADs could be a robust
tool for examining the efficacy of oMPAs for enhancing tuna biomass. As existing sampling
214 methods make it difficult to sample within oMPAs, dFADs could provide an effective alternative
215 (11, 2). CPUE-based methods, the main method currently used to sample tuna populations in
216 oceanic environments (35), are limited to fished areas as no-take MPAs are inaccessible to
217 fishing vessels. Thus, following the closure of an oMPA, these studies are limited to sample in
218 the vicinity of the MPA and cannot document the variation of tuna population inside the MPA
219 (2, 14). Tagging studies of tuna in oMPAs are an alternative, but they are limited by the low
220 number of tagged animals and the relatively short tag lifetime (35, 36). Although research
221 vessels are ideally suited to sampling inside and outside oMPAs, collecting data over long
222 time scales and in remote ocean locations is expensive. dFADs, already widely present in
223 many ocean areas, are potentially a powerful and low-cost approach to sample tuna inside
224 and outside oMPAs.
225

226 The current work is the first we know of to investigate the effect of oMPAs on tuna inside and
227 outside an oMPA, at similar temporal and spatial scales to CPUE studies. At a larger scale,
228 using the dFAD network to follow tuna populations throughout an entire ocean could bring
229 unparalleled insights into tuna movement and behavior (11). However, dFADs also have
230 negative ecological consequences, including potentially increasing the overexploitation of tuna
231 resources (37), damaging coral reefs following collisions (38), and their contribution to marine
232 pollution (39). However, answering scientific questions based on data from dFADs that are
233 already being deployed by the fishing industry is unlikely to increase their number. In fact, the
234 current collaboration between TNC and several purse seine fishing companies has had the
235 positive environmental outcome of reducing the impact of dFAD groundings on Palmyra's
236 sensitive coral reef ecosystem. This is because once the dFADs drifted into the vicinity of the
237 atoll, they were tracked so they could be retrieved prior to impact on the reef.

238 **Conclusion**

239 This study presents a new method to sample tuna inside and outside oMPAs, which will
240 hopefully be applied more frequently in the future. We found no discernable benefit of the
241 oMPA to tuna biomass, which may be explained by limited fishing immediately outside the
242 area. More studies of oMPAs using dFADs, particularly in areas that are heavily fished, are
199 **Caveats**

200 Our study has several caveats. First, biomass values at depth must be interpreted with some
201 caution as they could be overestimated. Tuna biomass is calculated using Satlink software
202 based on the target strength of 5 kg skipjack tuna, a species without a swimbladder. Thus,
203 fish with swimbladders such as yellowfin and bigeye tuna that are likely to be present deeper
204 in the water column could inflate the total tuna biomass estimates. Second, echosounders on
205 dFADs are configured to estimate aggregating tuna, which could exhibit different dynamics
206 than free-schooling tuna (34). Further, dFADs are designed to sample tuna, so other parts of
207 the ecosystem – including other fish species abundances – remain unknown. Third, each
208 observation by a dFAD is not truly independent of other observations from the same dFAD,
209 as tuna are following the platform and each dFAD is simultaneously an observing and
210 aggregating device. However, including the random effect for dFAD in the GAMM and the
211 large number of dFADs in the dataset (n=902) minimises this limitation.

212 **dFADs as a scientific tool**

213 The interpretability of many of the predictors in the model suggest dFADs could be a robust
tool for examining the efficacy of oMPAs for enhancing tuna biomass. As existing sampling
214 methods make it difficult to sample within oMPAs, dFADs could provide an effective alternative
215 (11, 2). CPUE-based methods, the main method currently used to sample tuna populations in
216 oceanic environments (35), are limited to fished areas as no-take MPAs are inaccessible to
217 fishing vessels. Thus, following the closure of an oMPA, these studies are limited to sample in
218 the vicinity of the MPA and cannot document the variation of tuna population inside the MPA
219 (2, 14). Tagging studies of tuna in oMPAs are an alternative, but they are limited by the low
220 number of tagged animals and the relatively short tag lifetime (35, 36). Although research
221 vessels are ideally suited to sampling inside and outside oMPAs, collecting data over long
222 time scales and in remote ocean locations is expensive. dFADs, already widely present in
223 many ocean areas, are potentially a powerful and low-cost approach to sample tuna inside
224 and outside oMPAs.
225

226 The current work is the first we know of to investigate the effect of oMPAs on tuna inside and
227 outside an oMPA, at similar temporal and spatial scales to CPUE studies. At a larger scale,
228 using the dFAD network to follow tuna populations throughout an entire ocean could bring
229 unparalleled insights into tuna movement and behavior (11). However, dFADs also have
230 negative ecological consequences, including potentially increasing the overexploitation of tuna
231 resources (37), damaging coral reefs following collisions (38), and their contribution to marine
232 pollution (39). However, answering scientific questions based on data from dFADs that are
233 already being deployed by the fishing industry is unlikely to increase their number. In fact, the
234 current collaboration between TNC and several purse seine fishing companies has had the
235 positive environmental outcome of reducing the impact of dFAD groundings on Palmyra's
236 sensitive coral reef ecosystem. This is because once the dFADs drifted into the vicinity of the
237 atoll, they were tracked so they could be retrieved prior to impact on the reef.

238 **Conclusion**

239 This study presents a new method to sample tuna inside and outside oMPAs, which will
240 hopefully be applied more frequently in the future. We found no discernable benefit of the
241 oMPA to tuna biomass, which may be explained by limited fishing immediately outside the
242 area. More studies of oMPAs using dFADs, particularly in areas that are heavily fished, are
necessary to answer the question of whether oMPAs enhance tuna stocks. Despite the objectives of oMPAs often, including the benefits to tuna fisheries (40), the primary objectives of oMPAs, and notably the PRIMNM (15), are to protect pelagic and benthic biodiversity and their ecosystem services (2). Palmyra Atoll has already proven to provide conservation benefits for sharks (41) and seabirds (42), but potential benefits for the broader pelagic ecosystem in PRIMNM are largely unknown. Ultimately, the benefits of oMPAs should be judged based on all their objectives, which include but are not exclusively on, the benefit to tuna fisheries.
necessary to answer the question of whether oMPAs enhance tuna stocks. Despite the objectives of oMPAs often, including the benefits to tuna fisheries (40), the primary objectives of oMPAs, and notably the PRIMNM (15), are to protect pelagic and benthic biodiversity and their ecosystem services (2). Palmyra Atoll has already proven to provide conservation benefits for sharks (41) and seabirds (42), but potential benefits for the broader pelagic ecosystem in PRIMNM are largely unknown. Ultimately, the benefits of oMPAs should be judged based on all their objectives, which include but are not exclusively on, the benefit to tuna fisheries.
251

# Material and Methods

252

## Data collection and processing

253

254

255

256

257

258

The dFAD manufacturer Satlink provided the dFAD position and biomass data in the US EEZ around Palmyra Atoll (a box with ~110 nautical mile sides), which included the PRIMNM protected area and the close vicinity (Fig. 1A). The Kiribati EEZ in the southeast was not sampled. The dataset only includes information from companies that were collaborating on the project, although these companies are likely to own the majority of dFADs in the area, as the primary fishing zones of other companies are farther afield.

259

260

261

262

263

264

265

266

267

268

269

Satlink dFADs provide data on date, position and fish biomass. Date and position data were initially every 24 h from July to December 2021, then upgraded from January 2022 to every 4 hours, to facilitate recovery operations. Biomass data were obtained every 2 hours through the dFAD-mounted SIMRAD echosounders acquisitions. As dFAD biomass and position data were not synchronised, we estimated the position of each biomass measurement at the same time as the biomass using a linear interpolation between the previous and the next position. Acquisitions of biomass data comprised an ensemble of 32 pings echo-integrated by layer over 5 min periods (28). The "backscattering volume strength" (Sv) obtained are stored and converted to biomass internally using the "target strength" of a horizontally oriented 5-kg skipjack tuna (Satlink, personal communication). The highest biomass value over the 2 hours period is then sent to the satellite and added to the dataset.

270

271

272

273

274

275

276

277

278

The echosounder range is from 3 to 115 m, with a 32° beamwidth, separated in 10 layers of 11.2 m, each with an independent biomass value. Only Sv values over -45 kHz are considered in the echo-integration to remove plankton and small fish from the acquisition. Biomass values <1 t by layer are neglected, and the echosounder saturates for biomass value >63 t by layer. Each dFAD is calibrated by the manufacturer in a tank before delivery. Two Satlink buoy types are included in the dataset: the ISL+ characterised by a SIMRAD ES12 transducer and emitting a pulse at 190.5 kHz and 120 W; and the SLX+ (the large majority of the dFADs), characterized by a SIMRAD ES16 transducer, emitting a modulated pulse at 200 kHz and 200 W.

279

280

281

282

283

284

285

286

287

288

289

290

291

Data were collected from 1st August 2021 to 30 April 2023. During this time, 902 dFADs with at least one position acquired in the sampled area were detected, with some dFADs crossing the area several times over the period. The longest, uninterrupted track in our data set is 65 days, although dFADs averaged 5 days in the sampling area. Following the recommendation of (43), we removed every dFAD with a speed >3 knots, considering that these are likely to be onboard a vessel. We have no previous information on the fishing history and soak time of dFADs before they entered our area. Further, we have no information on potential fishing events in our dataset. We considered every dFAD that left the oMPA for >3 days as a new dFAD in our analysis, to take account that it could have been fished whilst outside the area. We also assumed that the fish biomass measured by the dFADs is predominantly composed of tuna because the dFADs algorithm uses the backscattering of skipjack tuna as a reference. Target Strength (28) to transform acoustic data in biomass data, and there is strong evidence that most of the biomass recorded from dFADs is from tuna (12, 44).
251

# Material and Methods

252

## Data collection and processing

253

254

255

256

257

258

The dFAD manufacturer Satlink provided the dFAD position and biomass data in the US EEZ around Palmyra Atoll (a box with ~110 nautical mile sides), which included the PRIMNM protected area and the close vicinity (Fig. 1A). The Kiribati EEZ in the southeast was not sampled. The dataset only includes information from companies that were collaborating on the project, although these companies are likely to own the majority of dFADs in the area, as the primary fishing zones of other companies are farther afield.

259

260

261

262

263

264

265

266

267

268

269

Satlink dFADs provide data on date, position and fish biomass. Date and position data were initially every 24 h from July to December 2021, then upgraded from January 2022 to every 4 hours, to facilitate recovery operations. Biomass data were obtained every 2 hours through the dFAD-mounted SIMRAD echosounders acquisitions. As dFAD biomass and position data were not synchronised, we estimated the position of each biomass measurement at the same time as the biomass using a linear interpolation between the previous and the next position. Acquisitions of biomass data comprised an ensemble of 32 pings echo-integrated by layer over 5 min periods (28). The "backscattering volume strength" (Sv) obtained are stored and converted to biomass internally using the "target strength" of a horizontally oriented 5-kg skipjack tuna (Satlink, personal communication). The highest biomass value over the 2 hours period is then sent to the satellite and added to the dataset.

270

271

272

273

274

275

276

277

278

The echosounder range is from 3 to 115 m, with a 32° beamwidth, separated in 10 layers of 11.2 m, each with an independent biomass value. Only Sv values over -45 kHz are considered in the echo-integration to remove plankton and small fish from the acquisition. Biomass values <1 t by layer are neglected, and the echosounder saturates for biomass value >63 t by layer. Each dFAD is calibrated by the manufacturer in a tank before delivery. Two Satlink buoy types are included in the dataset: the ISL+ characterised by a SIMRAD ES12 transducer and emitting a pulse at 190.5 kHz and 120 W; and the SLX+ (the large majority of the dFADs), characterized by a SIMRAD ES16 transducer, emitting a modulated pulse at 200 kHz and 200 W.

279

280

281

282

283

284

285

286

287

288

289

290

291

Data were collected from 1st August 2021 to 30 April 2023. During this time, 902 dFADs with at least one position acquired in the sampled area were detected, with some dFADs crossing the area several times over the period. The longest, uninterrupted track in our data set is 65 days, although dFADs averaged 5 days in the sampling area. Following the recommendation of (43), we removed every dFAD with a speed >3 knots, considering that these are likely to be onboard a vessel. We have no previous information on the fishing history and soak time of dFADs before they entered our area. Further, we have no information on potential fishing events in our dataset. We considered every dFAD that left the oMPA for >3 days as a new dFAD in our analysis, to take account that it could have been fished whilst outside the area. We also assumed that the fish biomass measured by the dFADs is predominantly composed of tuna because the dFADs algorithm uses the backscattering of skipjack tuna as a reference. Target Strength (28) to transform acoustic data in biomass data, and there is strong evidence that most of the biomass recorded from dFADs is from tuna (12, 44).
292 Generalised Additive Mixed Model

293 Tuna biomass associated with dFADs is influenced by many temporal and spatial
294 environmental variables (13, 27, 45, 46). We use a Generalised Additive Mixed Model (GAMM,
295 Wood, 2020) to disentangle the spatial effect of the oMPA from the other predictors affecting
296 the tuna biomass. GAMMs are able to handle a large number of non-linear predictors, are
297 relatively robust to collinearity between predictors, and can include cyclic effects such as day-
298 night cycles (47).

299 Fish catch data are commonly zero inflated (i.e., the data contain an excess of zeros) (48, 49).
300 The majority of our fish biomass data from dFADs were also zeros. To account for the zero
301 inflation, it is typical to use a hurdle model (49). This method consists of multiplying two
302 separate models: one binomial model to predict the presence/absence of the interest
303 organism, a second to predict the positive values. However, this method can complicate
304 interpretation because two sub-models are combined (50). We thus used a Tweedie
305 distribution model that could handle zeros and positive data (50). A Tweedie distribution can
306 be considered a mix of a Poisson (Tweedie power parameter *p* = 1) and a Gamma (Tweedie
307 power parameter *p* = 2) distribution, where *p* will be fixed by the model between 1 and 2 (50).
308

308 We considered three temporal predictors. The first was the Time of day (hours), as tuna exhibit
309 a well-known daily aggregation/dispersion behaviour (51). The second temporal variable was
310 Season, to capture seasonal migrations of tuna (21), where tuna might leave or return to
311 Palmyra. The last temporal variable was Years, to investigate the inter-annual variability
312 between the two sampling years (August 2021 to July 2022 and August 2022 to July 2023)
313 and potential links of tuna biomass to the El Niño southern oscillation (29).

314 We included three spatial predictors in the GAMM. The first was MPA, which represents the
315 presence Inside/Outside of each dFAD position to investigate the potential effect of the oMPA
316 on tuna biomass. The second spatial predictor was the Layer depth (m), which represents the
317 different depths that tuna biomass were recorded. To capture the potential daily migration
318 pattern of tuna, we included an interaction between Depth Layer and Time of day, which could
319 also provide some clue to species composition, as different species prefer different depths
320 (44). The last spatial predictor was Distance to the nearest dFAD (km), because dFADs near
321 each other might share tuna biomass (33).

322 Environmental oceanographic variables also affect tuna biomass and distribution (27) and are
323 used by fishermen to optimise fishing. We used six environmental predictors in the model, four
324 of which represented for surface conditions: Salinity (unitless), Chlorophyll a (mg.m⁻³), Current
325 velocity (m.s⁻¹), and Sea surface temperature (°C). The other two environmental predictors
326 were Thermocline depth (m), because the position of thermocline is important for many tuna
327 species (26, 25), and Moon fraction (unitless), as moon illumination could potentially affect
328 tuna foraging behavior (31). Daily surface environmental predictors other than Moon fraction
329 were obtained from the model Copernicus (52), and had a grid resolution of 0.083°. Moon
330 fraction was calculated using the R package "suncalc". Finally, the model includes the dFADs
331 identification number as a random effect to capture individual differences in each dFAD in
332 terms of their physical and acoustic configuration and calibration, and the multiple data
333 generated from each dFAD.
292 Generalised Additive Mixed Model

293 Tuna biomass associated with dFADs is influenced by many temporal and spatial
294 environmental variables (13, 27, 45, 46). We use a Generalised Additive Mixed Model (GAMM,
295 Wood, 2020) to disentangle the spatial effect of the oMPA from the other predictors affecting
296 the tuna biomass. GAMMs are able to handle a large number of non-linear predictors, are
297 relatively robust to collinearity between predictors, and can include cyclic effects such as day-
298 night cycles (47).

299 Fish catch data are commonly zero inflated (i.e., the data contain an excess of zeros) (48, 49).
300 The majority of our fish biomass data from dFADs were also zeros. To account for the zero
301 inflation, it is typical to use a hurdle model (49). This method consists of multiplying two
302 separate models: one binomial model to predict the presence/absence of the interest
303 organism, a second to predict the positive values. However, this method can complicate
304 interpretation because two sub-models are combined (50). We thus used a Tweedie
305 distribution model that could handle zeros and positive data (50). A Tweedie distribution can
306 be considered a mix of a Poisson (Tweedie power parameter *p* = 1) and a Gamma (Tweedie
307 power parameter *p* = 2) distribution, where *p* will be fixed by the model between 1 and 2 (50).
308

308 We considered three temporal predictors. The first was the Time of day (hours), as tuna exhibit
309 a well-known daily aggregation/dispersion behaviour (51). The second temporal variable was
310 Season, to capture seasonal migrations of tuna (21), where tuna might leave or return to
311 Palmyra. The last temporal variable was Years, to investigate the inter-annual variability
312 between the two sampling years (August 2021 to July 2022 and August 2022 to July 2023)
313 and potential links of tuna biomass to the El Niño southern oscillation (29).

314 We included three spatial predictors in the GAMM. The first was MPA, which represents the
315 presence Inside/Outside of each dFAD position to investigate the potential effect of the oMPA
316 on tuna biomass. The second spatial predictor was the Layer depth (m), which represents the
317 different depths that tuna biomass were recorded. To capture the potential daily migration
318 pattern of tuna, we included an interaction between Depth Layer and Time of day, which could
319 also provide some clue to species composition, as different species prefer different depths
320 (44). The last spatial predictor was Distance to the nearest dFAD (km), because dFADs near
321 each other might share tuna biomass (33).

322 Environmental oceanographic variables also affect tuna biomass and distribution (27) and are
323 used by fishermen to optimise fishing. We used six environmental predictors in the model, four
324 of which represented for surface conditions: Salinity (unitless), Chlorophyll a (mg.m⁻³), Current
325 velocity (m.s⁻¹), and Sea surface temperature (°C). The other two environmental predictors
326 were Thermocline depth (m), because the position of thermocline is important for many tuna
327 species (26, 25), and Moon fraction (unitless), as moon illumination could potentially affect
328 tuna foraging behavior (31). Daily surface environmental predictors other than Moon fraction
329 were obtained from the model Copernicus (52), and had a grid resolution of 0.083°. Moon
330 fraction was calculated using the R package "suncalc". Finally, the model includes the dFADs
331 identification number as a random effect to capture individual differences in each dFAD in
332 terms of their physical and acoustic configuration and calibration, and the multiple data
333 generated from each dFAD.
# Acknowledgements

We would like to warmly thank our collaborators, the United States Purse Seine Tuna Group

(USPSTG) and Cape Fisheries Management Company for access to their dFAD networks,

without which this work would not have been possible. We would also thank the TNC Palmyra

Atoll Conservation Adaptation and Resilience Laboratory staff, for all their efforts on the

Palmyra FAD Watch Program.

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10. N. Ehrhardt, J. E. Brown, B. G. Pohlot, “Desk Review of FADs fisheries development in the WECAFC region and the impact on stock assessments” (WESTERN CENTRAL ATLANTIC FISHERY COMMISSION (WECAFC), 2017).

11. G. Moreno, et al., Fish aggregating devices (FADs) as scientific platforms. *Fisheries Research* **178**, 122–129 (2016).

12. B. Orue, et al., Aggregation process of drifting fish aggregating devices (DFADs) in the Western Indian Ocean: Who arrives first, tuna or non-tuna species? *PLoS ONE* **14**, e0210435 (2019).

13. Y. Baidai, L. Dagorn, M. J. Amande, D. Gaertner, M. Capello, Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices
# Acknowledgements

We would like to warmly thank our collaborators, the United States Purse Seine Tuna Group

(USPSTG) and Cape Fisheries Management Company for access to their dFAD networks,

without which this work would not have been possible. We would also thank the TNC Palmyra

Atoll Conservation Adaptation and Resilience Laboratory staff, for all their efforts on the

Palmyra FAD Watch Program.

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21. R. E. Shadwick, L. L. Schiller, D. S. Fudge, “Physiology of swimming and migration in tunas” in *Swimming Physiology of Fish: Towards Using Exercise to Farm a Fit Fish in Sustainable Aquaculture*, (Springer, 2013), pp. 45–78.

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23. T. J. Quinn, R. B. Deriso, *Quantitative fish dynamics* (oxford university Press, 1999).

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26. K. M. Schaefer, D. W. Fuller, B. A. Block, “Vertical Movements and Habitat Utilization of Skipjack (Katsuwonus pelamis), Yellowfin (Thunnus albacares), and Bigeye (Thunnus obesus) Tunas in the Equatorial Eastern Pacific Ocean, Ascertained Through Archival Tag Data” in *Tagging and Tracking of Marine Animals with Electronic Devices*, Reviews: Methods and Technologies in Fish Biology and Fisheries., (Springer Netherlands, 2009), pp. 121–144.

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38. L. Escalle, et al., Environmental versus operational drivers of drifting FAD beaching in the Western and Central Pacific Ocean. *Sci Rep* **9**, 14005 (2019).

39. R. Churchill, Just a Harmless Fishing Fad—or Does the Use of FADs Contravene International Marine Pollution Law? *Ocean Development & International Law* **52**, 169–192 (2021).

40. D. M. Kaplan, et al., Spatial management of Indian Ocean tropical tuna fisheries: potential and perspectives. *ICES J. Mar. Sci.* **71**, 1728–1749 (2014).

41. T. D. White, et al., Assessing the effectiveness of a large marine protected area for reef shark conservation. *Biological Conservation* **207**, 64–71 (2017).

42. H. S. Young, S. M. Maxwell, M. G. Conners, S. A. Shaffer, Pelagic marine protected areas protect foraging habitat for multiple breeding seabirds in the central Pacific. *Biological Conservation* **181**, 226–235 (2015).

43. B. Orue, et al., From fisheries to scientific data: A protocol to process information from fishers' echo-sounder buoys. *Fisheries Research* **215**, 38–43 (2019).
28. J. Lopez, G. Moreno, G. Boyra, L. Dagorn, A model based on data from echosounder buoys to estimate biomass of fish species associated with fish aggregating devices. *Fishery Bulletin* **114**, 166–178 (2016).

29. P. Lehodey, M. Bertignac, J. Hampton, A. Lewis, J. Picaut, El Niño Southern Oscillation and tuna in the western Pacific. *Nature* **389**, 715–718 (1997).

30. H. McGowan, A. Theobald, Atypical weather patterns cause coral bleaching on the Great Barrier Reef, Australia during the 2021–2022 La Niña. *Sci Rep* **13**, 6397 (2023).

31. J. Scutt Phillips, T. Peatman, L. Escalle, B. Leroy, N. Smith, Electronic tagging for the mitigation of bigeye and yellowfin tuna juveniles by purse seine fisheries in (2019).

32. R. Mugo, S.-I. Saitoh, A. Nihira, T. Kuroyama, Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective: Skipjack tuna habitat from RS & GIS in western NP. *Fisheries Oceanography* **19**, 382–396 (2010).

33. K. M. Stehfest, et al., Network analysis of acoustic tracking data reveals the structure and stability of fish aggregations in the ocean. *Animal Behaviour* **85**, 839–848 (2013).

34. X. Wang, et al., Impacts of fish aggregation devices on size structures of skipjack tuna Katsuwonus pelamis. *Aquat Ecol* **46**, 343–352 (2012).

35. T. B. Letessier, P. J. Bouchet, J. J. Meeuwig, Sampling mobile oceanic fishes and sharks: implications for fisheries and conservation planning: Sampling of oceanic predators. *Biol Rev* **92**, 627–646 (2017).

36. A. J. Richardson, et al., Residency and reproductive status of yellowfin tuna in a proposed large-scale pelagic marine protected area. *Aquatic Conserv: Mar Freshw Ecosyst* **28**, 1308–1316 (2018).

37. L. Dagorn, K. N. Holland, V. Restrepo, G. Moreno, Is it good or bad to fish with FAD s? What are the real impacts of the use of drifting FAD s on pelagic marine ecosystems? *Fish Fish* **14**, 391–415 (2013).

38. L. Escalle, et al., Environmental versus operational drivers of drifting FAD beaching in the Western and Central Pacific Ocean. *Sci Rep* **9**, 14005 (2019).

39. R. Churchill, Just a Harmless Fishing Fad—or Does the Use of FADs Contravene International Marine Pollution Law? *Ocean Development & International Law* **52**, 169–192 (2021).

40. D. M. Kaplan, et al., Spatial management of Indian Ocean tropical tuna fisheries: potential and perspectives. *ICES J. Mar. Sci.* **71**, 1728–1749 (2014).

41. T. D. White, et al., Assessing the effectiveness of a large marine protected area for reef shark conservation. *Biological Conservation* **207**, 64–71 (2017).

42. H. S. Young, S. M. Maxwell, M. G. Conners, S. A. Shaffer, Pelagic marine protected areas protect foraging habitat for multiple breeding seabirds in the central Pacific. *Biological Conservation* **181**, 226–235 (2015).

43. B. Orue, et al., From fisheries to scientific data: A protocol to process information from fishers' echo-sounder buoys. *Fisheries Research* **215**, 38–43 (2019).
44. J. Lopez, G. Moreno, L. Ibaibarriaga, L. Dagorn, Diel behaviour of tuna and non-tuna species at drifting fish aggregating devices (DFADs) in the Western Indian Ocean, determined by fishers' echo-sounder buoys. *Mar Biol* **164**, 44 (2017).

45. M. Capello, et al., Population assessment of tropical tuna based on their associative behavior around floating objects. *Sci Rep* **6**, 36415 (2016).

46. B. Orue, et al., Comparing the distribution of tropical tuna associated with drifting fish aggregating devices (DFADs) resulting from catch dependent and independent data. *Deep Sea Research Part II: Topical Studies in Oceanography* **175**, 104747 (2020).

47. S. N. Wood, Inference and computation with generalized additive models and their extensions. *Test* **29**, 307–339 (2020).

48. S. Arcuti, et al., Spatio-temporal modelling of zero-inflated deep-sea shrimp data by Tweedie generalized additive. *Statistica* **73**, 87–101 (2013).

49. J. T. Thorson, Three problems with the conventional delta-model for biomass sampling data, and a computationally efficient alternative. *Can. J. Fish. Aquat. Sci.* **75**, 1369–1382 (2018).

50. Y. Li, Y. Jiao, Q. He, Decreasing uncertainty in catch rate analyses using Delta-AdaBoost: An alternative approach in catch and bycatch analyses with high percentage of zeros. *Fisheries Research* **107**, 261–271 (2011).

51. P. Fréon, L. Dagorn, Review of fish associative behaviour: Toward a generalisation of the meeting point hypothesis. *Reviews in Fish Biology and Fisheries* **10**, 183–207 (2000).

52. European Union-Copernicus Marine Service, Global Ocean 1/12° Physics Analysis and Forecast updated Daily. Mercator Ocean International. https://doi.org/10.48670/MOI-00016. Deposited 2016.
44. J. Lopez, G. Moreno, L. Ibaibarriaga, L. Dagorn, Diel behaviour of tuna and non-tuna species at drifting fish aggregating devices (DFADs) in the Western Indian Ocean, determined by fishers' echo-sounder buoys. *Mar Biol* **164**, 44 (2017).

45. M. Capello, et al., Population assessment of tropical tuna based on their associative behavior around floating objects. *Sci Rep* **6**, 36415 (2016).

46. B. Orue, et al., Comparing the distribution of tropical tuna associated with drifting fish aggregating devices (DFADs) resulting from catch dependent and independent data. *Deep Sea Research Part II: Topical Studies in Oceanography* **175**, 104747 (2020).

47. S. N. Wood, Inference and computation with generalized additive models and their extensions. *Test* **29**, 307–339 (2020).

48. S. Arcuti, et al., Spatio-temporal modelling of zero-inflated deep-sea shrimp data by Tweedie generalized additive. *Statistica* **73**, 87–101 (2013).

49. J. T. Thorson, Three problems with the conventional delta-model for biomass sampling data, and a computationally efficient alternative. *Can. J. Fish. Aquat. Sci.* **75**, 1369–1382 (2018).

50. Y. Li, Y. Jiao, Q. He, Decreasing uncertainty in catch rate analyses using Delta-AdaBoost: An alternative approach in catch and bycatch analyses with high percentage of zeros. *Fisheries Research* **107**, 261–271 (2011).

51. P. Fréon, L. Dagorn, Review of fish associative behaviour: Toward a generalisation of the meeting point hypothesis. *Reviews in Fish Biology and Fisheries* **10**, 183–207 (2000).

52. European Union-Copernicus Marine Service, Global Ocean 1/12° Physics Analysis and Forecast updated Daily. Mercator Ocean International. https://doi.org/10.48670/MOI-00016. Deposited 2016.
Figures

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** **Study context.** (A) Map of the "Pacific Remote Island Marine National Monument" (PRIMNM) in the Pacific Ocean, with an inset of the US exclusive economic zone around Palmyra Atoll. (B) A schematic of a drifting fish aggregating device (dFAD). (C) The 902 dFAD trajectories in the current study – the colours are used to help distinguish individual dFAD tracks.</figcaption></figure>
Figures

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Study context. (A) Map of the "Pacific Remote Island Marine National Monument" (PRIMNM) in the Pacific Ocean, with an inset of the US exclusive economic zone around Palmyra Atoll. (B) A schematic of a drifting fish aggregating device (dFAD). (C) The 902 dFAD trajectories in the current study – the colours are used to help distinguish individual dFAD tracks.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2. Map of tuna biomass (t) in the region.** This is based on an inverse distance weighted smoothing of the collected by the 902 dFADs. The Pacific Remote Island Marine National Monument (in red) and the position of Kingman Reef and Palmyra Atoll and the National Wildlife Refuge boundaries (in green).</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2. Map of tuna biomass (t) in the region.** This is based on an inverse distance weighted smoothing of the collected by the 902 dFADs. The Pacific Remote Island Marine National Monument (in red) and the position of Kingman Reef and Palmyra Atoll and the National Wildlife Refuge boundaries (in green).</figcaption></figure>
<figure><img src="image_4.png" /></figure>

**Fig. 3.** Variation in tuna biomass (y-axis) as a function of the predictors in the Generalised Additive Mixed Model (GAMM). Confidence bands shown in grey. Tuna biomass is similar inside and outside the oMPA. Note that the larger the y-axis range in the GAMM, the more important the predictor.
<figure><img src="image_4.png" /></figure>

**Fig. 3.** Variation in tuna biomass (y-axis) as a function of the predictors in the Generalised Additive Mixed Model (GAMM). Confidence bands shown in grey. Tuna biomass is similar inside and outside the oMPA. Note that the larger the y-axis range in the GAMM, the more important the predictor.
# Supplementary

## S1: Model summary

### summary(gam_mod_tweedie season)

Family: Tweedie (p=1.354)

Link function: log

Formula:

$$ \text{Biomass} \sim \begin{aligned}[t] & s(\text{time\_in\_dataset}, k = 6, \text{bs} = \text{"cr"}) + s(\text{Chl}, \text{bs} = \text{"cr"}, k = 6) + s(\text{sal}, k = 6, \text{bs} = \text{"cr"}) + s(\text{Speed}, k = 6, \text{bs} = \text{"cr"}) + s(\text{temperature}, k = 6, \text{bs} = \text{"cr"}) + s(\text{thermocline\_depth}, k = 6, \text{bs} = \text{"cr"}) + s(\text{current\_velocity}, k = 6, \text{bs} = \text{"cr"}) + s(\text{time\_day}, k = 6, \text{by} = \text{Layer}, \text{bs} = \text{"cc"}) + s(\text{FAD\_Name}, \text{bs} = \text{"re"}) + s(\text{Moon\_Illumination}, k = 6, \text{bs} = \text{"cr"}) + s(\text{distance\_to\_nearest\_dFAD}, k = 6, \text{bs} = \text{"cr"}) + \text{Layer} + \text{season} + \text{years} + \text{presence\_in\_MPA} \end{aligned} $$

### Parametric coefficients:

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>t value</th><th>Pr(&gt;|t|)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-4.416802</td><td>0.053592</td><td>-82.416</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer2</td><td>-0.149578</td><td>0.036097</td><td>-4.144</td><td>3.42e-05 ***</td></tr><tr><td>LayerLayer3</td><td>1.035769</td><td>0.030963</td><td>33.452</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer4</td><td>2.001544</td><td>0.028474</td><td>70.293</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer5</td><td>2.402102</td><td>0.027870</td><td>86.188</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer6</td><td>2.863229</td><td>0.027309</td><td>104.847</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer7</td><td>3.147134</td><td>0.027041</td><td>116.382</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer8</td><td>3.325923</td><td>0.026902</td><td>123.629</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer9</td><td>3.475327</td><td>0.026752</td><td>129.909</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer10</td><td>3.675726</td><td>0.026556</td><td>138.415</td><td>&lt; 2e-16 ***</td></tr><tr><td>seasonspring</td><td>-0.223275</td><td>0.029355</td><td>-7.606</td><td>2.83e-14 ***</td></tr><tr><td>seasonsummer</td><td>0.329701</td><td>0.042712</td><td>7.719</td><td>1.17e-14 ***</td></tr><tr><td>seasonwinter</td><td>-0.560837</td><td>0.023387</td><td>-23.980</td><td>&lt; 2e-16 ***</td></tr><tr><td>years2</td><td>-0.396840</td><td>0.045419</td><td>-8.737</td><td>&lt; 2e-16 ***</td></tr><tr><td>presence\_in\_MPA1</td><td>-0.022149</td><td>0.007755</td><td>-2.856</td><td>0.00429 **</td></tr></tbody></table>

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

### Approximate significance of smooth terms:

<table><thead><tr><th></th><th>edf</th><th>Ref.df</th><th>F p-value</th></tr></thead><tbody><tr><td>s(time\_in\_dataset)</td><td>4.794</td><td>4.968</td><td>143.56 &lt;2e-16 ***</td></tr><tr><td>s(Chl)</td><td>1.008</td><td>1.016</td><td>38.79 &lt;2e-16 ***</td></tr><tr><td>s(sal)</td><td>1.005</td><td>1.009</td><td>55.21 &lt;2e-16 ***</td></tr><tr><td>s(Speed)</td><td>4.614</td><td>4.925</td><td>14.19 &lt;2e-16 ***</td></tr><tr><td>s(temperature)</td><td>4.493</td><td>4.884</td><td>16.81 &lt;2e-16 ***</td></tr><tr><td>s(thermocline\_depth)</td><td>4.318</td><td>4.702</td><td>12.70 &lt;2e-16 ***</td></tr><tr><td>s(current\_velocity)</td><td>4.800</td><td>4.976</td><td>37.75 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer1</td><td>3.962</td><td>4.000</td><td>192.28 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer2</td><td>3.882</td><td>4.000</td><td>39.61 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer3</td><td>3.976</td><td>4.000</td><td>219.50 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer4</td><td>3.983</td><td>4.000</td><td>466.33 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer5</td><td>3.993</td><td>4.000</td><td>1036.05 &lt;2e-16 ***</td></tr></tbody></table>
# Supplementary

## S1: Model summary

### summary(gam_mod_tweedie season)

Family: Tweedie (p=1.354)

Link function: log

Formula:

$$ \text{Biomass} \sim \text{s(time\_in\_dataset, k = 6, bs = "cr") + s(Chl, bs = "cr", k = 6) + s(sal, k = 6, bs = "cr") + s(Speed, k = 6, bs = "cr") + s(temperature, k = 6, bs = "cr") + s(thermocline\_depth, k = 6, bs = "cr") + s(current\_velocity, k = 6, bs = "cr") + s(time\_day, k = 6, by = Layer, bs = "cc") + s(FAD\_Name, bs = "re") + s(Moon\_Illumination, k = 6, bs = "cr") + s distancetonearest dFAD, k = 6, bs = "cr") + Layer + season + years + presence\_in\_MPA} $$

### Parametric coefficients:

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>t value</th><th>Pr(&gt;|t|)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-4.416802</td><td>0.053592</td><td>-82.416</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer2</td><td>-0.149578</td><td>0.036097</td><td>-4.144</td><td>3.42e-05 ***</td></tr><tr><td>LayerLayer3</td><td>1.035769</td><td>0.030963</td><td>33.452</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer4</td><td>2.001544</td><td>0.028474</td><td>70.293</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer5</td><td>2.402102</td><td>0.027870</td><td>86.188</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer6</td><td>2.863229</td><td>0.027309</td><td>104.847</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer7</td><td>3.147134</td><td>0.027041</td><td>116.382</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer8</td><td>3.325923</td><td>0.026902</td><td>123.629</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer9</td><td>3.475327</td><td>0.026752</td><td>129.909</td><td>&lt; 2e-16 ***</td></tr><tr><td>LayerLayer10</td><td>3.675726</td><td>0.026556</td><td>138.415</td><td>&lt; 2e-16 ***</td></tr><tr><td>seasonspring</td><td>-0.223275</td><td>0.029355</td><td>-7.606</td><td>2.83e-14 ***</td></tr><tr><td>seasonsummer</td><td>0.329701</td><td>0.042712</td><td>7.719</td><td>1.17e-14 ***</td></tr><tr><td>seasonwinter</td><td>-0.560837</td><td>0.023387</td><td>-23.980</td><td>&lt; 2e-16 ***</td></tr><tr><td>years2</td><td>-0.396840</td><td>0.045419</td><td>-8.737</td><td>&lt; 2e-16 ***</td></tr><tr><td>presence\_in\_MPA1</td><td>-0.022149</td><td>0.007755</td><td>-2.856</td><td>0.00429 **</td></tr></tbody></table>

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

### Approximate significance of smooth terms:

<table><thead><tr><th></th><th>edf</th><th>Ref.df</th><th>F p-value</th></tr></thead><tbody><tr><td>s(time\_in\_dataset)</td><td>4.794</td><td>4.968</td><td>143.56 &lt;2e-16 ***</td></tr><tr><td>s(Chl)</td><td>1.008</td><td>1.016</td><td>38.79 &lt;2e-16 ***</td></tr><tr><td>s(sal)</td><td>1.005</td><td>1.009</td><td>55.21 &lt;2e-16 ***</td></tr><tr><td>s(Speed)</td><td>4.614</td><td>4.925</td><td>14.19 &lt;2e-16 ***</td></tr><tr><td>s(temperature)</td><td>4.493</td><td>4.884</td><td>16.81 &lt;2e-16 ***</td></tr><tr><td>s(thermocline\_depth)</td><td>4.318</td><td>4.702</td><td>12.70 &lt;2e-16 ***</td></tr><tr><td>s(current\_velocity)</td><td>4.800</td><td>4.976</td><td>37.75 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer1</td><td>3.962</td><td>4.000</td><td>192.28 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer2</td><td>3.882</td><td>4.000</td><td>39.61 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer3</td><td>3.976</td><td>4.000</td><td>219.50 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer4</td><td>3.983</td><td>4.000</td><td>466.33 &lt;2e-16 ***</td></tr><tr><td>s(time\_day):LayerLayer5</td><td>3.993</td><td>4.000</td><td>1036.05 &lt;2e-16 ***</td></tr></tbody></table>
<table><tr><td>s(time_day):LayerLayer6</td><td>3.996</td><td>4.000</td><td>1990.71</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer7</td><td>3.997</td><td>4.000</td><td>2872.14</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer8</td><td>3.998</td><td>4.000</td><td>3485.07</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer9</td><td>3.998</td><td>4.000</td><td>3835.83</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer10</td><td>3.998</td><td>4.000</td><td>4137.63</td><td>&lt;2e-16 ***</td></tr><tr><td>s(FAD_Name)</td><td>692.118</td><td>715.000</td><td>63.48</td><td>&lt;2e-16 ***</td></tr><tr><td>s(Moon_Illumination)</td><td>4.174</td><td>4.699</td><td>220.32</td><td>&lt;2e-16 ***</td></tr><tr><td>sensetivity</td><td>4.843</td><td>4.981</td><td>60.48</td><td>&lt;2e-16 ***</td></tr></table>

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1

R-sq. (adj) = 0.0995 Deviance explained = 38%

fREML = 4.6917e+06 Scale est. = 5.1715 n = 1532821
<table><tr><td>s(time_day):LayerLayer6</td><td>3.996</td><td>4.000</td><td>1990.71</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer7</td><td>3.997</td><td>4.000</td><td>2872.14</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer8</td><td>3.998</td><td>4.000</td><td>3485.07</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer9</td><td>3.998</td><td>4.000</td><td>3835.83</td><td>&lt;2e-16 ***</td></tr><tr><td>s(time_day):LayerLayer10</td><td>3.998</td><td>4.000</td><td>4137.63</td><td>&lt;2e-16 ***</td></tr><tr><td>s(FAD_Name)</td><td>692.118</td><td>715.000</td><td>63.48</td><td>&lt;2e-16 ***</td></tr><tr><td>s(Moon_Illumination)</td><td>4.174</td><td>4.699</td><td>220.32</td><td>&lt;2e-16 ***</td></tr><tr><td>sensetivity</td><td>4.843</td><td>4.981</td><td>60.48</td><td>&lt;2e-16 ***</td></tr></table>

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1

R-sq. (adj) = 0.0995 Deviance explained = 38%

fREML = 4.6917e+06 Scale est. = 5.1715 n = 1532821
S2: Fishing effort

<figure><img src="image_5.png" /></figure>

**Fig. S1.** Mean Fishing effort (in fishing hours) between 2012 and 2020 from the "Global Fishing Watch" (GFW, https://globalfishingwatch.org/), calculated from fishing boats Automatic Identification System (AIS). In red the Marine National Monument around Palmyra Atoll and Kingman reef. Note that fishing effort from purse seine fisheries are underestimated in the data set, as the short time equates to relatively few fishing hours. The majority of the fishing effort represented here are from longline fisheries.
S2: Fishing effort

<figure><img src="image_5.png" /></figure>

**Fig. S1.** Mean Fishing effort (in fishing hours) between 2012 and 2020 from the "Global Fishing Watch" (GFW, https://globalfishingwatch.org/), calculated from fishing boats Automatic Identification System (AIS). In red the Marine National Monument around Palmyra Atoll and Kingman reef. Note that fishing effort from purse seine fisheries are underestimated in the data set, as the short time equates to relatively few fishing hours. The majority of the fishing effort represented here are from longline fisheries.