The Impact of Marine Heatwaves on Isotherm Displacement and Tuna Distribution in Vanuatu

Abstract

Marine heatwaves (MHWs) have intensified in frequency, duration, and severity over recent decades. These events, defined by unusually warm sea surface temperatures (SSTs), can cause significant ecological impacts. This is particularly so for Pacific Island countries, such as Vanuatu, where communities rely on marine resources for their food and livelihoods. A common ecological response to MHWs is the movement of oceanic species to cooler waters. Predicting such shifts through monitoring SST isotherms can help identify thermal boundaries that marine species favor. This study explores the connection between MHWs, SST isotherm movement, and tuna abundance in Vanuatu. The displacement of the 28 °C isotherm was analyzed across three major MHW events (2008–2009, 2016, and 2021–2022). It was found that MHWs with longer duration and greater intensity caused more significant isotherm displacement. Additionally, the El Niño–Southern Oscillation had an important influence on MHW formation and isotherm movement. The effects of these displacements on tuna distribution varied between events. The ability to monitor MHWs and SST isotherm movement could be an effective instrument for the prediction of areas of suppressed or abundant tuna activity and can be used to aid in the proactive management of food security and fishery sectors.

1. Introduction

Small Island Developing States (SIDSs) are at high risk of impacts of climate variability and change. Due to their remoteness and limited resource bases, SIDSs have high exposure to exogenous shocks, such as climatic hazards, and high vulnerability to extreme climate events [1]. Such events can pose catastrophic risks to terrestrial and marine flora and fauna, as well as to the communities dependent on these natural ecosystems. This is most acutely seen in SIDSs in the Pacific, where ecosystems critically provide communities with food and income security [2].

Vanuatu, a SIDS in the southwestern Pacific, is one of the most at-risk nations for natural disasters, ranking first in the World Risk Report in 2020 [3]. It is estimated that Vanuatu’s average annual economic loss per capita due to natural disasters is approximately 21% of Vanuatu’s total annual GDP, around $167 annually [4]. Due to anthropogenic climate change, Vanuatu faces long-term climate risks, such as ocean warming, ocean acidification, and sea level rise, which are compounded by the risks of extreme climate events, including marine heatwaves (MHWs), tropical cyclones, and drought [1]. Combined, these climate risks cascade and compound into significant sectoral and livelihood impacts, threatening Vanuatu’s long-term economic and social welfare.

Approximately 94% of Vanuatu’s population resides within 5 km of the coast [5]. Vanuatu’s culture, livelihood, food, and income security are closely intertwined with marine ecosystems. Fish is a primary source of protein and food security source for households in Vanuatu. The nutritional demand for fish will continue to increase with Vanuatu’s population growth, placing increasing pressure on Vanuatu’s fisheries to produce and catch enough fish for local consumption and international exports [6,7]. Vanuatu’s fisheries sector involves commercial offshore fishing and coastal subsistence fishing (including recreational and artisanal). The commercial fisheries sector includes Vanuatu’s Exclusive Economic Zone (EEZ), the 820,000 km² maritime zone beyond and adjacent to the territorial sea [8]. The EEZ extends 200 nautical miles from Vanuatu’s coast. While Vanuatu’s commercial fisheries sector is estimated to account for 1% of Vanuatu’s annual GDP, subsistence fishing is considered to significantly contribute to household well-being, especially in rural areas of Vanuatu. Given the critical relationship of marine resources to Vanuatu’s livelihoods, disturbances to marine ecosystems can create widespread economic and sociocultural impacts for communities.

MHWs are discrete periods of prolonged anomalously warm sea surface temperatures (SSTs). Hobday et al. (2016) define an MHW event as “discrete, prolonged anomalously warm water event” such that SSTs exceed the “90th percentile threshold for five or more days with no more than two below-threshold days” in between [9]. This definition has now become widely accepted and adopted in MHW literature, with several studies examining the frequency, duration, and intensity of MHWs within a specific period and location.

Since the start of the Industrial Revolution, and particularly from the 1950s, anthropogenic climate change has amplified the intensity, frequency, and duration of MHWs globally [10]. The fraction of the global ocean surface that has experienced an MHW event over one year increased from 50% to 80% in the period between 1982 and 2023 [11]. The impact of these MHW trends is expected to be felt most intensely by Pacific Island Countries (PICs), such as Vanuatu (see [12,13]). Oliver et al. (2019) warn that in a high emissions scenario, PICs are likely to experience a “permanent MHW state” as early as 2040 [14]. In regions with increasing climate risk and warming SSTs, projected extreme MHW conditions are very likely to disturb marine ecosystems, communities, and industries in the near future.

Vanuatu’s climate is strongly influenced by the interaction between atmospheric and oceanic conditions, with low seasonal variability in air temperature and relatively high seasonal variability in precipitation. The country has two distinct seasons: a warm, wet season from November to April and a cooler, dry season from May to October. The range in average monthly maximum air temperatures throughout the year is about 4 °C for Port Vila and 5 °C for Aneityum [15]. Monthly average ocean temperature around Vanuatu ranges from 25.5 °C in August to 29 °C in February [15]. In saying this, monthly SSTs can be 2 °C greater or less than these averages throughout a given year. Seasonal air temperature changes in Vanuatu are strongly connected to changes in the surrounding ocean temperature. The El Niño Southern Oscillation (ENSO) is a dominant interannual climate mode in the Pacific [16]. Earlier studies demonstrated that stronger MHWs near the equator typically occur during El Niño, while stronger MHW events typically occur during La Niña in the south, north, and west Pacific regions [17]. While the monitoring and prediction of MHW events is becoming increasingly comprehensive, the ability to predict the ecological impacts of MHWs remains relatively novel.

MHW events can lead to a range of marine ecological impacts, including coral bleaching, direct marine species mortality due to heat stress, indirect vertebrate and invertebrate death due to dissolved oxygen concentrations, and degradation of kelp and seagrass meadows [18,19,20]. These ecological impacts create flow-on disruptions to industries and communities, primarily fisheries management and productivity [21,22]. Investigating the broad range of biological impacts of MHWs, Smale et al. (2023) provide case studies of MHWs resulting in losses for predator-prey dynamics, aquaculture industries, coral reef bleaching events, and mass mortality events [23]. In Vanuatu, coral bleaching events have been reported following MHWs, with increasing MHW intensity and frequency exacerbating thermal stress for coral reefs around Vanuatu’s waters [24]. In 2010–2011, a severe MHW in Western Australia, termed the “Ningaloo Niño,” resulted in maximum SST anomalies exceeding 3 °C [22]. As a result, mass fish deaths, poor invertebrate growth, and decreases in commercial fish stock were reported in the following months [22,25]. Similarly, between 2013 and 2014, an unprecedented MHW event in California, colloquially referred to as “The Blob,” altered the biogeography and biomass of commercial fish stocks, resulting in the closure of economically important fisheries [26]. Observing MHWs in three PICs, Holbrook et al. (2022) further found that under increasing emission scenario pathways, MHWs are going to increase in intensity, duration, and frequency, with the extent of the increase dependent on the degree of warming [13]. This was found to have adverse implications for PICs, including reduced fish protein in household diets and reduced fisheries catches. These wide-ranging ecological impacts can cause far-reaching perturbations to marine ecosystems, risking marine fauna and flora health [11,27].

A common ecological response to anomalously high SSTs is the temporary relocation and redistribution of marine species. Temperature-driven distribution tends to be highly variable and unpredictable across marine species due to the differential bio-physiological abilities to efficiently respond to warming ocean conditions [28,29]. Mobile marine species contain the unique ability to alter their distribution and movement patterns to find their preferred niches, i.e., favourable foraging grounds, thermal water ranges, and habitats [30]. Pelagic fish, in particular, are projected to be amongst the most negatively impacted by extreme warming events such as MHWs. Facing sustained MHW conditions, pelagic fish are expected to either “adapt, move or die” [14,30]. In the short term, behavioural changes, such as relocation polewards or vertically through the water column, are expected, as well as range contraction to habitats that satisfy their thermal niche and preference [31]. Lehodey et al. (1997) demonstrated how altered tuna distribution occurs as a result of dynamic climate processes shifting the position of SST isotherms [32]. The authors investigated how the position of the 29 °C SST isotherm altered skipjack abundance in the northern Pacific Ocean. In their study, the location of the 29 °C isotherm was used as a proxy for the WPWP cold-tongue convergence zone, an area of nutrient-rich waters and subsequent high tuna abundance. It was found that ENSO events led to a large zonal displacement of the 29 °C isotherm, which was consistent with significant shifts in skipjack tuna catch and abundance. This research was foundational in establishing a relationship between tuna movement and ENSO. However, there remains a dearth of literature outlining how this relationship manifests during MHWs.

Jacox et al. (2022) examined how global MHWs affect the distribution of mobile marine species, a phenomenon referred to as “thermal displacement” [33]. Thermal displacement is defined as “the minimum distance that must be travelled away from a MHW to track constant sea surface temperature” [33]. The Eastern Tropical Pacific was identified as a particular area of concern as the potential displacement of species can reach up to 2000 km per degree of SST anomaly. That is, as SST anomalies increase, marine fauna must migrate further away to find favourable SST niches. Notwithstanding its novelty, this study is limited by its general approach to examining the relationship between MHWs and species distribution. Therefore, a gap in the MHW literature remains; there is a need for a more focused assessment of the responses of specific species to MHWs and the cascading impacts that may ensue for communities and industries.

This study will focus on the impact of MHWs on the location and movement of SST isotherms. As tuna fish are expected to move to their preferred thermal niche during periods of increased SSTs, the location of SST isotherms can act as an effective indicator of potential tuna movement and areas of abundance. Improving our understanding of the behaviour of tuna during MHWs is necessary to examine the potential impacts on the Vanuatu region’s fisheries sector and improve the management and preparedness for such extreme climate events.

This study aims to evaluate the impact of MHW events on tuna distribution and abundance. This study hypothesises that MHW events will lead to a latitudinal shift of the 28 °C SST isotherm, which in turn will alter tuna distribution, resulting in areas of suppressed and/or abundant tuna activity. With a focus on the Vanuatu region, this study aims to enhance the monitoring and prediction of the ecological impacts of MHWs. By providing insights into species distribution during periods of extreme SSTs, the findings can assist fisheries and marine managers in taking proactive measures to prepare and respond to future MHWs. Given the expected intensification of MHWs, this information is crucial for ensuring the long-term resilience of Vanuatu’s fisheries sector.

2. Materials and Methods

2.1. Study Area

MHW analysis was conducted for the Vanuatu region (165°–175° E, 5°–25° S; Figure 1). This study area was selected to account for the fact that tuna move and migrate in open ocean waters, typically away from coastal areas. The larger spatial area includes Vanuatu’s Exclusive Economic Zone (EEZ), which can provide closer insights into how offshore commercial fishing activities may be impacted by MHW events.

2.2. Data

2.2.1. Observational Sea Surface Temperature Data

The SST data used in this study is obtained from the U.S. National Oceanic and Atmospheric Administration (NOAA) 0.25° daily Optimum Interpolation Sea Surface Temperature v2-1 dataset (hereinafter dOISST). The dOISST dataset is compiled from a range of satellite and in situ observations that are interpolated at a 0.25° global grid. The data has been available since September 1981 and is updated daily. Yang et al. (2021) found that dOISST was effective at capturing modes of climate variability in the tropical Pacific [34]. Recent analysis by Holbrook et al. (2022) found that the dOISST data was very close to the actual SSTs recorded in PICs, including Fiji, Palau, and Samoa [13]. In this study, the dOISST dataset was utilised to identify and examine MHWs in the Vanuatu region. This study utilised pre-processed dOISST data and functions created and provided by the Climate Information Services for Resilient Development in Vanuatu (Van CISRDP) project.

2.2.2. Fish Stock Assessment Data

This study chose to focus on tuna species to investigate MHW-driven species distribution in the Vanuatu region. Tuna is one of the major species caught in Vanuatu’s EEZ and is a primary source of income for Vanuatu (see [35,36]). To assess tuna distribution and abundance, this study utilised fish stock assessment data provided by the Vanuatu Fisheries Department.

The most complete and current Pacific Island fisheries datasets are provided by the Western and Central Pacific Fisheries Commission (WCPFC). The WCPFC was established by the Convention for the Conservation and Management of Highly Migratory Fish Stocks across the Western and Central Pacific Ocean. Vanuatu is a cooperating member of the WCPFC. The WCPFC provides public domain aggregated catch-effort data using operational, aggregate, and annual catch estimates data provided by commission members. Tuna longline fishing data was obtained from the WCPFC for the period 1980–2018. The longline dataset is aggregated by month and year at a 5° × 5° spatial resolution [37]. The longline data was selected for its completeness and the ability to discern Vanuatu’s EEZ within the given spatial area. Longline fishing methods have operated for several decades across all oceans, primarily targeting commercial pelagic fish, including tuna species. A number of studies have utilised longline fishing datasets with broad spatio-temporal coverage to indicate preferred tuna habitats and tuna distribution patterns [38,39].

The longline fish stock assessment data included two metrics of fish catch: volume of fish caught (tonnes) and fish catch (numbers). This study utilised the latter, fish catch (numbers), as a proxy for tuna abundance and distribution for the following reasons. A key assumption in this study is that tuna caught in the Vanuatu region were adults rather than larvae or juvenile fish. Tuna species have been found to show a clear pattern of migration for spawning, feeding, and breeding [40]. The age of tuna significantly changes the length and weight of the fish [41,42]. Given that no further information is provided by the WCPFC as to the age of the tuna fish caught in the dataset, this study could not account for the seasonal migration of tuna for spawning, feeding, and breeding purposes. The catch in numbers is not affected by the weight and age of the tuna in the fish stock assessment dataset. Moreover, temperature has been found to impact the weight of tuna; thus, it would be unclear if temperature changes had altered fish abundance or the displacement of the 28 °C isotherm [43].

2.3. Data Analysis

2.3.1. MHW Data Analysis

Following Hobday et al. (2016) recommendation, a 30-year base period was selected to calculate the daily 90th percentile threshold and climatological mean for the Vanuatu region [9]. This study utilised pre-processed dOISST data from a previous study in the region [44] to calculate the 90th percentile threshold and climatological mean from 1982 to 2022 in the Vanuatu region. To assess the intensity of MHW events, the pre-processed data adopted categorisation method outlined by Hobday et al. (2018) [45]. MHWs are categorised based on the multiples of the difference between the climatological mean and the 90th percentile threshold for a selected time frame in a given location. The four categories—Moderate, Strong, Severe. and Extreme—reflect the increasing intensity of SST anomalies (SSTAs) based on the long-term climatology of Vanuatu’s ocean waters.

MHW events that occurred between 1982 and 2022 were visualised and assessed using functions created and provided by the Van-KIRAP project [44]. The pre-processed data includes the daily climatology, 90th percentile threshold, and relevant MHW intensity categories for the study period 1982 to 2022. This study assesses the following MHWs that occurred in the Vanuatu region based on the following variables:

• Duration: number of days of an event.
• Mean intensity: average temperature above the climatological mean during an event.
• Maximum intensity: maximum temperature above the climatological mean during an event.
• Cumulative intensity: sum of the daily intensities above the climatological mean for the duration of an event.

These variables were calculated for each MHW event between 1982 and 2022. The MHW event characteristics may differ slightly from similar analyses presented in [44] due to the defined study area being broader and inclusive of offshore regions for analysis.

2.3.2. Isotherm Location and Analysis

To investigate the relationship between MHWs and tuna distribution, this study uses the location of the 28 °C SST isotherm (hereinafter 28 °C isotherm) as a proxy for areas of high tuna abundance. Monitoring the 28 °C isotherm can be an effective mechanism for predicting the potential redistribution and relocation of tuna during periods of anomalously high SSTs. This study tracked the displacement of the 28 °C isotherm before, during, and after MHW events in the Vanuatu region. This study defines displacement as a change in the location of the 28 °C isotherm before and after an MHW event. This is measured by a change in the latitudinal value of the position of the 28 °C isotherm. The latitudinal value was selected for analysis instead of the combined latitude and longitude coordinates because this study aimed to examine the extent and intensity of extremely high warm water distribution. This approach provides a greater indication of areas with unfavourable and favourable thermal niches for tuna species.

A 14-day rolling average was applied to the pre-processed dOISST climatological data to calculate the coordinates of the 28 °C isotherm. The average climatological position of the 28 °C isotherm was 14.63° S in the Vanuatu region between 1982 and 2022. The average isotherm position varies month to month, spending 6 months of the calendar year south of the average position and 6 months north of the average position (Appendix A) (Figure 2).

This study applied a 14-day rolling average to the raw dOISST data to determine the coordinates of the 28 °C isotherm during an MHW event. To examine the degree of isotherm displacement during a MHW event, the coordinates of the 28 °C isotherm location at the start and end date of an MHW were calculated. The position of the 28 °C isotherm was then plotted onto maps of the Vanuatu region. The spatial plotting functions used in this study were created for and provided by the Van-KIRAP study with some minor edits to visualise and plot the 28 °C isotherm. The latitude value at the end date position was then subtracted from the latitude value at the start date position. This indicates how far southwards or northwards the 28 °C isotherm moved during an MHW event.

The displacement of the 28 °C isotherm can then be compared across MHW events, which can allow for closer examination of the relationship between MHW duration and intensity and isotherm displacement. For instance, MHW events of a longer duration are expected to cause a larger displacement of the 28 °C isotherm when comparing the isotherm position at the start and end date.

2.4. Distribution of Tuna in Relation to Isotherm Positioning

It has been well-established that temperature is an integral factor in the distribution of pelagic fish [46,47]. To monitor how tuna species respond to changing water temperature, the 28 °C isotherm was selected to represent the preferable thermal niche of yellowfin, albacore, and bigeye tuna species. A key assumption in this study is that albacore, bigeye, and yellowfin tuna will respond homogenously to MHWs and will swim at the same depth with the 28 °C isotherm. This study acknowledges that tuna move both horizontally and vertically through the water column (see [48]). However, this study has not accounted for the vertical distribution of tuna due to its use of SST data, which does not reflect the total depth of the water column.

Although tuna species are highly sensitive to changes in temperature, the thermal preferences of tuna species vary (

Table 1. Preferable thermal ranges for albacore, bigeye, and yellowfin tuna.

Tuna	Species Name	Lower Limit (°C)	Upper Limit (°C)	Reference
Yellowfin	Katsuwonus pelamzs	18	33	[51]
Albacore	Thunnus germo	12–14	22	[52]
Bigeye	Thunnus obesus	16	22	[53]

). Previous research investigating the impact of SST on tuna movement found that thermal preferences were aligned with isotherm movement [49,50,51]. While the 28 °C isotherm exceeds the thermal upper limits of albacore tuna, it has been found that the distribution of these fish corresponds to the location of the 23 °C isotherm in the austral winter and the 28 °C isotherms in the austral summer [13]. Given that MHWs typically occur during austral summer months (November–February), the 28 °C isotherm is an effective indicator of albacore distribution. Bigeye tuna have preferable temperature ranges between 25–29 °C in waters shallower than 100 m [50]. As this study utilises SST, the 28 °C isotherm can be used to monitor this thermal preference. Lastly, studies have found that yellowfin tuna preferably move in surface mixed layers with a thermal range between 20 °C to 27 °C [50,51].

To measure whether changes in tuna abundance align with the movement of the 28 °C isotherm, this study focused on the 10–15° S band of the 5° × 5° grid cells in the fish stock assessment dataset. This band was selected because the climatological position of the 28 °C isotherm is 14.63° S; thus, this was considered as a reference area for the study. It is predicted that during an MHW event, tuna abundance (measured by the fish stock assessment data) will decrease in this band as the 28 °C isotherm moves away from its climatological position. For example, if the 28 °C isotherm moved 3° southwards during an MHW, it will have moved out of the 10–15° S band, and hence, the tuna catch is predicted to decrease in this area.

2.5. Accounting for Seasonality

To account for seasonal migration patterns displayed by tuna species, a time series decomposition was performed to remove seasonality and trends from the fish stock assessment dataset [54,55]. This study utilised the Python (version 3.11.7) library statsmodels.tsa.seasonal, specifically the seasonal_decompose package. Time series decomposition allows for greater detection of anomaly values and analysis of the impact of exogenous factors, such as extreme climate events [56,57]. The time series for yellowfin, albacore, and bigeye tuna were decomposed into trend, seasonality, and residual (anomaly) components. An additive model was used to complete the time series decomposition due to the presence of negative values in the seasonal component of the fish stock assessment data. In this dataset, negative values indicate points in time when the observed fish stock is lower than the predicted trend. Following the completion of the time series decomposition for each species, the outliers in each time series were identified using an interquartile range method and removed from the decomposed dataset. Given that the additive model was the best fit for the fish stock assessment data, this model was also applied to dOISST data for decomposition.

The seasonality and trend components were subtracted from the dOISST and fish stock assessment datasets, such that residual values remained. In this study, the residual values for the dOISST data and the fish stock assessment data were selected to represent SSTAs, which can indicate MHW events, and fish catch anomalies, respectively. While it is acknowledged that MHW events are a combination of warming climate trends and SSTAs, the residual values were selected to more closely examine whether SST variability and anomalies influence variability in tuna fish catch once trend and seasonality have been accounted for.

3. Results

3.1. Regional Analysis

3.1.1. MHW Analysis

It was found that the Vanuatu region experienced 80 MHW events between 1982 and 2022 (Figure 3) (Appendix B).

In the Vanuatu region, an increase in MHW frequency is evident. One MHW event was recorded in the first decade of the study period in December 1984. MHW frequency rose to 18 events between 1992 and 2002 and then 19 events between 2003 and 2012. However, from 2013 to 2022, the number of MHW events more than doubled to 40 events.

The degree of displacement of the 28 °C isotherm was calculated for the MHW events recorded in the Vanuatu region between 1982 and 2022. The displacement was calculated as the difference between the latitude value at the start date and the latitude value at the end date of the MHW. Of the MHW events recorded, 61 MHWs recorded a southwards displacement of the 28 °C isotherm, 13 MHWs recorded a northwards displacement of the 28 °C isotherm, and 6 MHWs did not result in displacement of the isotherm. The 10 MHW events with the largest southward isotherm displacement for the period 1982 to 2022 are presented in

Table 2. 28 °C isotherm displacement during MHW events (1982–2022).

Start Date	End Date	Duration, Days	Latitude Value on Start Date, ° S	Latitude Value on End Date, ° S	Isotherm Displacement (End Date–Start Date), ° S
11 October 2022	8 January 2023	90	15.625	21.875	6.25
31 October 2008	11 January 2009	73	17.625	23.625	6.0
8 July 2022	8 January 2023	185	17.125	−22.875	5.75
22 September 2016	8 December 2016	78	13.125	18.625	5.5
19 November 2001	12 December 2001	24	15.875	21.125	5.25
17 December 1997	29 December 1997	13	17.125	21.625	4.5
12 December 1984	26 December 1984	15	17.875	21.875	4.0
16 November 2005	1 December 2005	16	15.875	19.625	3.75
2 December 2011	16 December 2011	15	16.125	19.625	3.5
16 January 2015	12 March 2015	56	20.875	24.375	3.5

.

3.1.2. Monthly Tuna Catch During MHWs (1995–2018)

As only one MHW was recorded prior to 1995, Figure 4 displays the anomaly monthly catches of yellowfin, albacore, and bigeye tuna between 1995 and 2018. The MHW events during this period are displayed by the overlaid red areas.

3.2. Case Study Periods

From the regional analysis in Section 3.2, three MHW events for the study period from 1982 to 2022 were selected for further analysis: 2008–2009, 2016, and 2021–2022.

As the fish stock assessment data is only available until 2018, the periods 2008–2009 and 2016 were chosen to examine whether long and intense MHW events led to a displacement of the 28 °C isotherm and changes in fish. Prior research conducted by Bhardwaj and Kuleshov (2024) also noted that spikes in MHW frequency, duration, and intensity were recorded during these periods, justifying additional analysis [44].

The 2008–2009 period recorded the MHW with the highest cumulative intensity prior to 2018, as well as recording the second-highest maximum intensity. The 2008–2009 period is the only year in the study period where one MHW occurred, which can allow for greater insight into how long it takes for the 28 °C isotherm to return to its climatological position. The period of 2016 was chosen as this year because it recorded the second-highest cumulative intensity prior to 2018. It has also been reported as a highly impactful event in Vanuatu.

The period 2021–2022 was chosen as it recorded the largest number of MHW days, highest maximum, mean, and cumulative intensity. While fish stock assessment data was not made available for this period, analysis of the impact of isotherm displacement is useful to predicting changes in tuna abundance.

Analysis and presentation of the isotherm displacement during these MHW case study periods are presented in this section. Analysis of the SSTA and MHW category of each case study period is displayed in Appendix C.

3.2.1. 2008–2009 MHW

The MHW beginning on 31 October 2008 and lasting until 11 January 2009 recorded a duration of 73 days, mean intensity of 1.2 °C, and cumulative intensity of 88.75 °C. The MHW recorded a Moderate intensity for 66 days (2 November 2008 to 6 January 2009) and a Strong intensity (Category 2) that lasted from 14 and 15 December 2008 (Figure 5).

The October 2008 MHW event recorded the second-largest isotherm displacement in the study period. Between 31 October 2008 and 11 January 2009, the 28 °C isotherm was displaced 6° southwards, moving from 17.63° S to 23.63° S (Figure 6). The isotherm reached the southernmost point of 23.63° S by the end date of the MHW period. The MHW reached a Strong intensity on 14 December 2008, and the 28 °C isotherm was located at 21.63° S.

The residual monthly tuna catches for yellowfin, albacore, and bigeye tuna varied between 31 October 2008 and 11 January 2009 (Figure 7). The monthly yellowfin tuna catches increased until December 2008 and then continued to decrease until the end of the MHW event (Figure 7a). A similar increase and then decrease in the monthly albacore catches is presented in Figure 7b. However, between November and December 2008, the residual monthly albacore catch values remained positive. This indicates that the actual observed albacore catch was higher than the expected catch predicted from the seasonal and trend components. From December 2008, residual monthly albacore catch values decrease to negative, demonstrating that the actual observed catch was lower than predicted. Bigeye tuna catch presents a contrasting trend to that observed for the monthly yellowfin and albacore catches. While monthly bigeye tuna catches increase throughout the MHW event, the residual monthly catch values remain negative, indicating that the actual observed catch was less than predicted for the 2008 MHW (Figure 7c).

No further MHWs were recorded in the Vanuatu region from January 2009 to August 2010. The average latitude position of the 28 °C isotherm in 2009 was 15.50° S, compared to the average latitude position in 2008 of 16.37° S. From 11 January 2009 (end date of the October 2008 MHW), the 28 °C isotherm reached its southernmost point of 25.13° S on 27 February 2009 (Figure 8a). Between 27 February and 31 December 2009, the 28 °C isotherm moved 10.5° northwards to 14.63° S (Figure 8b). With no further MHW events recorded, this isotherm relocated 10.5° northwards to 14.63° S. This relocation is near the climatological position of the isotherm in December: 15.78° S.

3.2.2. 2016 MHW

In 2016, five MHW events occurred, totalling 130 MHW days (Figure 9). 2016 recorded the third-largest number of MHW days per year in the study period.

The January 2016 MHW recorded a duration of 27 days, mean intensity of 1.07 °C, and cumulative intensity of 28.99 °C. Between 24 January and 19 February 2016, the 28 °C isotherm was displaced southwards by 2.75°, moving from its starting location at 21.34° S to its final location at 24.13° S (Figure 10).

The monthly tuna catches for yellowfin, albacore, and bigeye tuna displayed a consistent decrease between 24 January to 19 February 2016 (Figure 11). It can be observed that monthly yellowfin, albacore, and bigeye tuna catches decrease from positive to negative residual values. This demonstrates that prior to the MHW event, the actual observed values were higher than what is predicted by the trend and seasonal components. As the MHW progresses, the residual monthly tuna catches decrease to negative values, indicating that the actual observed tuna catch was lower than the predicted tuna catch expected from the trend and seasonal components.

The September 2016 MHW recorded a duration of 78 days, mean intensity of 0.99 °C, and cumulative intensity of 77.55 °C. Between 22 September and 8 December 2016, the 28 °C isotherm moved 5.5° southwards from its starting location of 13.13° S to its final location of 18.63° S (Figure 12).

The monthly tuna catches for yellowfin, albacore, and bigeye tuna displayed a consistent decrease between 22 September and 8 December 2016 (Figure 13). It can be observed that the actual observed monthly tuna catches were above the expected catch predicted by the trend and seasonal components. As the MHW progresses, the monthly tuna catches decrease to negative residual values, indicating that the actual observed tuna catch was lower than the predicted tuna catch expected from the trend and seasonal components.

3.2.3. 2021–2022 MHW Period

Between 2021 and 2022, 11 MHW events were recorded, resulting in a total of 407 MHW days (Figure 14a). Between August 2021 and January 2023, an MHW event was recorded every month (except May 2022). The 28 °C isotherm moved outside the EEZ boundary during 7 of the 11 MHW events between 2021 and 2022, and the number of days that the 28 °C isotherm was outside of Vanuatu’s EEZ was 154 days. In 2022, the Vanuatu region experienced an unprecedented MHW event (Figure 14b). Beginning on 8 July 2022, the MHW event lasted 185 days, reaching a maximum intensity of 2.07 °C, mean intensity of 1.40 °C, and a cumulative intensity of 259.14 °C.

The location of the 28 °C isotherm on the start date of the 2022 MHW event (8 July 2022) was 17.13° S. By the end date of the MHW event (8 January 2023), the 28° isotherm was 22.87° S (Figure 15). The total isotherm displacement was 5.75°, the third-largest displacement in the study period. The 28 °C isotherm reached its southernmost point of 23.13° S on 19 December 2022. Fish stock assessment data is unavailable for this time period.

3.3. Correlation Analysis

3.3.1. Relationship Between MHW Variables and Isotherm Displacement

Accounting for all MHWs in the selected study period, a Pearson correlation analysis was undertaken to determine whether MHW variables can predict isotherm displacement (

Table 3. Pearson correlation coefficient values between isotherm displacement and MHW variables. Two asterisks (**) indicate significance at a 99% confidence interval.

	Isotherm Displacement	Mean Intensity	Maximum Intensity	Cumulative Intensity	Duration
Isotherm displacement	1.0	0.61 **	0.66 **	0.59 **	0.6 **

). The correlation between isotherm displacement and MHW variables was found to have a moderate to strong position correlation. This suggests that isotherm displacement increases as duration, mean intensity, maximum intensity, and cumulative intensity increase. The values were further found to be significant at 1% confidence interval, indicating that the relationship is highly statistically significant.

3.3.2. Relationship Between Monthly Tuna Catch and SSTAs

The relationship between monthly tuna catch anomalies and monthly SSTAs for the study period 1995–2022 was analysed using Pearson correlation and least square regression analyses (Figure 16). Additional statistical analysis can be found in Appendix E.

4. Discussion

4.1. Influence of MHWs on Isotherm Displacement

An increase in the duration, intensity, and frequency of MHWs in the Pacific region has been evidenced over the past decades and is projected to intensify under varying climate emission scenarios in the future [11,14]. This study found that the Vanuatu region had experienced a total of 80 MHW events over the period 1982–2022. This total number of MHWs is slightly lower than the total observed in other PICs (see [13]). In the Vanuatu region, only one MHW event was recorded prior to 1992. Between 1992 and 2002, the number of MHWs increased to 18. Between 2003 and 2012, the Vanuatu region recorded 19 MHWs. Between 2012 and 2022, the Vanuatu region recorded 40 MHW events. The significant increase in MHW events per decade can be attributed to the increase in frequency of MHW events per year. For instance, approximately two MHW events occurred between 2003 and 2012, whereas four MHW events occurred per year between 2013 and 2022. This increase in approximately two events across the two decades aligns with Oliver et al. (2018), who found that the number of MHW events in the tropical Pacific region increased by two to three events between 2000 and 2016 [11]. Furthermore, the average mean intensity and cumulative intensity have increased over the last 40 years (mean intensity of 0.95 °C and cumulative intensity of 14.87 °C between 1982 and 2022; mean intensity of 1.10 °C, and cumulative intensity of 20.67 °C between 2003 and 2022).

Much of this increase in MHW frequency and intensity can be attributed to the gradual increase in SSTs associated with anthropogenic global warming and climate change [37]. The influence of anthropogenic global warming has resulted in shorter return periods of MHWs, hindering marine organisms’ ability to recover from extreme events [38,39]. During MHW events, SSTs reached intensities similar to those expected from high carbon emission scenarios [5]. These extreme events therefore reveal insights into the possible response of tuna to shifting SST isotherms and the opportunities and challenges for Pacific Island fisheries in the coming decades.

This study found that during the case study periods, MHW events with the highest intensity generally also led to the largest displacement of the 28 °C isotherm. Notably, MHWs with the highest cumulative intensity values resulted in the most negative displacement of the 28 °C isotherm. The unprecedented July 2022 MHW exemplifies the relationship between extreme MHW intensity and isotherm displacement. When the 28 °C isotherm reached the southernmost point during the July 2022 MHW, over 32% of the Vanuatu region was classified as experiencing a Strong MHW, and approximately 0.3% of the area was classified as Severe. This suggests that prolonged exposure to high temperatures in the ocean’s surface layer may have expanded the region of accumulated heat, thus pushing SST isotherms further outward from their climatological position.

It is important to note that increased MHW intensity is often combined with prolonged MHW events. The MHW starting in July 2022 lasted 184 days at a Moderate intensity. This MHW resulted in a southwards 28 °C isotherm displacement of 5.75°. This suggests that although MHW intensity was not in an extreme state, the duration of the prolonged high SSTAs may have a compounding effect on isotherm displacement. The combined impact of increased duration and intensity on isotherm displacement can further be evidenced in the October 2021 MHW and the October 2008 MHW, which both recorded the largest isotherm displacement in the study period. The combined influence of duration and intensity on isotherm displacement may be attributed to an additional factor: spatial scale. Bian et al. (2024) demonstrated MHWs decrease in intensity, frequency, and duration with increasing spatial scale [58]. The authors demonstrated that oceanic processes were found to be dominant drivers of small-scale MHWs, while atmospheric processes were key drivers of large-scale MHWs. While this study did not consider spatial scale, future research can benefit from determining the role of spatial scale in SST isotherm displacement and examining the underpinning oceanic and/or atmospheric processes driving MHWs in the Vanuatu region. A notable exception to this trend identified in the selected case study periods is the September 2016 MHW that recorded the third-longest duration and fourth-largest isotherm displacement. Compared to the other MHWs, the mean intensity of this MHW was ranked twenty-third in the study period. The disparate findings from the September 2016 MHW highlight the complexity of attributing isotherm displacement to a specific MHW variable. The correlation analysis between MHW variables and isotherm displacement reinforces this complexity.

Examining all the MHW events that occurred between 1982–2022 in the Vanuatu region, ENSO was found to be a primary driver of MHW events, specifically the phase of La Niña or neutral phases. It was found that 92.5% of MHW events occurred in La Niña or neutral phases (Appendix D). Thirty-four MHWs were recorded during a neutral phase of ENSO. During neutral ENSO conditions, enhanced thermal stratification insulates warm surface waters from colder, deeper waters, which in turn may have increased the accumulation of warm SSTs in the Vanuatu region. The increased likelihood and formation of MHWs may have been attributed to other air-sea heat exchange processes, such as the Indian Ocean Dipole and Madden–Julian Oscillation [43,59]. The interaction between these climate drivers and ENSO may also contribute to the increased MHW occurrence during ENSO-neutral years [43,45]. Increased wind and precipitation in nearby ocean areas can further affect the formation and movement of wave trains that transport warm water westward, such as the Rossby and Kelvin waves. Further research is required to examine the combined influence of these ocean-atmosphere processes on MHW formation in the Vanuatu region.

Given that La Niña enhances the conditions observed during an ENSO-neutral period, it is unsurprising that half of the MHW events in the study period coincided with La Niña. Due to the dipole-climate pattern of the tropical western and central Pacific Ocean, La Niña conditions in the South Pacific Ocean are typically associated with reduced convective cloud coverage and evaporation off the ocean waters. These factors can contribute to the intense MHWs observed in the Vanuatu region. These findings are supported by Holbrook et al. (2022), who demonstrated that MHW events in Fiji, Samoa, and Palau tended to coincide most commonly with La Niña periods [13,49]. Furthermore, the extent of isotherm displacement was generally larger during MHW events that coincided with La Niña periods. The strengthening trade winds and subsequent westward accumulation of warm water during La Niña expands the Pacific Ocean area with high SSTAs, in turn displacing the 28 °C isotherm from its climatological position. The associated upwelling of warm water due to strong trade winds can further drive SSTAs around the Vanuatu region, increasing the likelihood of MHWs.

Again, the September 2016 MHW is a notable exception to this trend. This MHW event recorded a high duration and cumulative intensity, with a subsequent 5.5° southward displacement of the 28 °C isotherm. Unlike the other MHW events with larger isotherm displacements, the September 2016 MHW event occurred during an El Niño phase. This can possibly be attributed to the extreme nature of 2016, which recorded the second-highest number of MHW days (130 MHW days) and second-highest number of MHW events in a given year (5 MHWs). The four preceding MHWs may have contributed to the significant southward displacement of the 28 °C isotherm, hence the deviation from the overall trend. Notwithstanding this exception, the relationship between ENSO and MHW events appears robust, and utilising the status of ENSO can be a valuable tool in predicting future MHW events and isotherm displacement. Further research can enhance the understanding of this relationship by examining the climatological position of the 28 °C isotherm during El Niño, La Niña, and neutral phases. This study demonstrated that the position of the 28 °C isotherm during MHW events was consistently displaced from its climatological position. It is expected that during the El Niño, La Niña, and neutral years, the 28 °C isotherm moves to its El Niño, La Niña, or neutral climatological position.

4.2. Isotherm Displacement and Tuna Distribution

MHW-driven species distribution is most commonly attributed to the relocation and redistribution of preferable thermal conditions. Monitoring the displacement of the 28 °C isotherm during MHW events can enable greater prediction of tuna movement before, during, and after MHW events. The impact of MHWs on fish stock was highly variable among the tuna species and across MHW events. While both the October 2008 and September 2016 MHW events led to significant southward displacements of 6° and 5.5°, respectively, the fish stock assessment data varied greatly. For instance, the October 2008 MHW event led to an increase and subsequent decrease in yellowfin and albacore catch, while resulting in a consistent increase in bigeye catch. This can potentially be attributed to a lagged relationship between MHWs and tuna abundance. The ecological impacts of MHWs can be short- and long-term, with decreases in tuna catch possibly only becoming apparent after an MHW has occurred. For instance, Caputi et al. (2014) demonstrated the ecological impacts of MHWs may only occur in the months and years following the extreme warming event [60]. Following the 2010–2011 Ningaloo Niño event in Western Australia, altered invertebrate recruitment patterns were observed in the two years after the event and shifts in community structure of marine organisms were observed eight months after the heat wave [51]. Lagged impacts can further include changes to predator-and-prey relationships due to MHW-induced habitat loss and range compression [31]. MHW periods have been found to drive predator redistribution, perturbing energy fluxes between upper and lower trophic levels in marine ecosystems [61,62]. Redistribution of tuna species away from Vanuatu’s waters can alter the marine food web, leading to inconsistent and unpredictable catch profiles, a novel challenge for fisheries management. Future research is encouraged to examine the lagged correlation between MHW events and ecological responses.

As mentioned earlier, 2016 is a notable year in terms of MHW frequency, intensity, duration, and isotherm displacement. During this MHW, a decrease in the monthly tuna catches for yellowfin, albacore, and bigeye tuna can be observed (Figure 10). These findings align with associated marine finfish and invertebrate deaths reported across the wider Pacific region. The Pacific Community (2016) reported that dead fish and invertebrates were observed floating near Efate, Port Vila, and Aneityum Island [63]. The January 2016 MHW recorded a maximum intensity of 1.32 °C. Local news reported that high SSTs combined with low tides were a potential catalyst for the mass fish kills [64].

The September 2016 MHW event (22 September–8 December) led to a decrease in monthly catches for all three tuna species (Figure 12). In 2016, 130 MHW days were recorded, compared to 73 MHW days recorded in 2008. The increase in MHW days in 2016 compared to 2008 may have impacted other environmental factors, which can shift tuna distribution in the Vanuatu region. While SST is a key determinant of tuna movement and biogeography, other exogenous factors can impact preferable tuna habitats and, thus, areas of high tuna abundance. Tropical tuna, including yellowfin, albacore, and bigeye tunas, prefer lower oxygen environments, higher sea surface height, and lower chlorophyll concentrations [65,66,67]. MHW events have been found to alter these oceanic conditions; thus, it is unclear whether changes in tuna abundance are attributed to the MHW-driven isotherm displacement. It is recommended that future studies investigate whether changes in tuna distribution is in fact attributed to SSTAs or instead caused by other consequences of heating waters. This can further clarify when these impacts begin to emerge and whether changes in tuna distribution are observed in the short- or long-term. Such research can inform fisheries managers about the necessary timing for preparation and response if MHWs are forecasted to occur. Moreover, it is important to note that the fish stock assessment data was collected via a range of data collection protocols across different regions and time periods, thus introducing issues with the reliability and validity of the dataset. Inconsistencies in catch rate reporting in the fish stock assessment dataset may have contributed to the insignificant results yielded in the correlation analysis between monthly SSTAs and monthly tuna catches. Given these considerations, future research is encouraged to use accurate datasets with greater reliability and validity to ascertain more clearly the relationship between MHW events, SST isotherm displacement, and relative tuna abundance remains unclear.

Over the coming decades, increased MHW duration and occurrence is likely to displace the 28 °C isotherm outside of Vanuatu’s EEZ more frequently and for longer periods of time, creating emerging challenges for marine managers and fisheries industries. This was most notable during the 2021 to 2022 period when 11 MHWs were recorded, totalling 407 MHW days. During this period, the 28 °C isotherm was outside Vanuatu’s EEZ for 154 days. The unprecedented nature of the July 2022 to January 2023 MHW and subsequent isotherm displacement (5.75° southwards) would have likely significantly altered the tuna catch across the Vanuatu region. As fish stock assessment data is unavailable for the 2021 to 2023 period, ecological impacts from other MHW events of a similar magnitude (i.e., number of Moderate and Strong days, maximum and mean intensity) could provide insight into the ecological repercussions of the July 2022 MHW and the cascading impacts for fisheries.

4.3. Potential for Monitoring and Prediction

The ability to predict the ecological impacts of MHWs can provide tangible benefits to local communities, fisheries managers, and policymakers [2]. As MHW events were found to coincide with ENSO-neutral and La Niña phases, seasonal forecasting of climate information that from dynamical probabilistic seasonal forecasts can further be utilised to predict MHW frequency and isotherm displacement. For fisheries and marine managers, this integrated forecasting can indicate potential areas of tuna abundance and inform strategic management decisions regarding tuna catch quotes, timing of fishing, and number of fishing vessels required throughout the Vanuatu region [68]. Aquaculture industries have modelled effective MHW preparation for the fisheries sector. After experiencing a strong MHW in 2016, the Tasmanian salmon aquaculture industry added alternative MHW metrics, such as maximum temperature and increased spatial resolution, to their MHW forecasts. These additional metrics improved the prediction of future MHWs such that when Tasmania experienced a strong MHW in 2017, aquaculture industries had already implemented proactive management strategies to mitigate potential ecological impacts. This included early harvest feed adjustments, thinning populations and relocating salmon pens to cooler areas [69,70]. Although Vanuatu’s tuna fishing industry is more expansive than aquaculture, this case study highlights the benefit of integrating alternative MHW metrics, such as isotherm displacement, into MHW forecasting.

The relationship between MHW and isotherm displacement can present both challenges and opportunities to Vanuatu’s fisheries sector. During most recorded MHWs in this study, the 28 °C isotherm was displaced southwards, which in turn is predicted to increase tuna catch in the southern regions of Vanuatu. By monitoring this southwards displacement, fisheries can potentially capitalise on increased tuna abundance in these areas by relocating fishing vessels and increasing catch effort during MHW periods. In the longer term, MHW events in the Pacific will have variable impacts on tuna abundance, which can exacerbate existing food security challenges already predicted under worsening climate change scenarios [7,71]. Integrating isotherm displacement into management practices can aid in securing adequate fishing catch, critically improving Vanuatu fisheries’ resilience into the future.

4.4. Limitations and Future Directions for Research

While the displacement of the 28 °C isotherm was apparent for the selected MHW case study periods, it remains unclear whether this results in a decrease in tuna abundance. The fish stock assessment data was coarse and contained missing values, which were then removed during the pre-processing of the data, which in turn may have altered the distribution of the dataset. This study did not have access to WCPFC metadata or information on the data collection methodology and protocols. As such, the accuracy and reliability of this dataset may be limited. Additionally, information regarding the longline gear depth and size was not publicly shared by the WCFPC. Without this information, the vertical movement of tuna cannot be accounted for in this study. Furthermore, this study can be expanded upon by examining the impact of MHWs below the ocean surface layer. Previous studies utilising in situ Argo floats have demonstrated that MHWs can occur at depths below surface level [72]. Measuring the impacts of MHWs at depth on tuna distribution could be achieved through monitoring how isotherms shift at various depths and whether this predicts tuna distribution across the water column.

This study can serve as an avenue for developing an early warning system (EWS) for MHWs in the Vanuatu region. The opportunity to monitor MHW events and subsequent isotherm displacement can enable closer prediction of tuna distribution. However, viewing MHW events and the associated ecological impacts as an isolated event can be detrimental. A multi-hazard EWS that monitors several extreme climate events can enhance the prediction of compounding and cumulative stressors. In doing so, industries and local communities can be better equipped to proactively prepare for and manage climate variability events. For instance, if an MHW event is closely followed by heavy rainfall (e.g., in La Niña years), the fishing and agriculture sector are likely to experience deleterious economic and environmental challenges. An integrated EWS could inform both sectors that a wet La Niña period is likely to lead to the occurrence of such events and provide management strategies to proactively reduce risks and effectively prepare for extreme climate events. This study provides foundational evidence for the impact of MHWs on tuna distribution, which can be utilised in a future integrated EWS, likely aiding in the preparedness and adaptive capacity of Vanuatu’s industries in the face of increasing climate variability and change.

5. Conclusions

The global increase in MHW frequency, duration, and intensity is a critical challenge facing marine ecosystems and species. As MHWs have become more frequent and intense, changes in tuna distribution and abundance are expected to ensue. This study found that duration and intensity resulted in further displacement of the 28 °C isotherm during MHWs in the Vanuatu region. The MHW events with the largest isotherm displacement were found to have coincided with La Niña and neutral conditions of ENSO. This suggests that the westward accumulation of warm sea surface waters contribute to displacement of the 28 °C isotherm during MHW events. Further research examining the climatological position of the 28 °C isotherm during ENSO years is recommended.

The effect of MHWs and isotherm displacement on monthly tuna catch varied across the MHW events. Several factors may have contributed to these mixed results. The greater number of MHW days in 2016 compared to 2008 may have impacted other environmental factors, such as the dissolved oxygen concentration, which could have contributed to altered tuna distribution during this MHW period [73]. The impact of MHWs on tuna distribution may be lagged such that changes in tuna catch may only be observed well after an MHW has occurred. Inconsistencies in the fish stock assessment dataset may have further contributed to the insignificant correlation found between monthly SSTAs and monthly tuna catches. Further analysis is hence required to fully understand the influence of MHWs on tuna distribution. Nonetheless, clearly as MHWs intensify, SSTs will more frequently exceed tuna’s thermal thresholds, causing a likely redistribution and relocation away from Vanuatu’s coastal and offshore waters. This can pose critical challenges for Vanuatu’s fisheries sector and the local communities dependent on tuna as a source of protein, livelihood, and income security.

The ability to monitor SST isotherm displacement can enhance the prediction of tuna distribution and movement during MHW periods. Incorporating this information into existing fisheries management and plans can enable greater preparation for MHW-driven species distribution. Moreover, as isotherm displacement and MHWs have been shown to correspond to ENSO phases, using dynamical seasonal forecasts that integrate all regionally relevant climate drivers, such as ENSO, to output a probabilistic SST and MHW forecasts, can aid in the prediction of changing SST isotherms. This can provide advanced warning of the potential ecological impacts of MHWs. This study provides a foundation for an EWS that identifies, monitors, and predicts MHWs and the subsequent impact on tuna distribution. This information can contribute to the management of tuna in the Vanuatu region and aid in building the resilience of Vanuatu’s fisheries industry into the future.