Next Article in Journal
BugTracker: Software for Tracking and Measuring Arthropod Activity
Next Article in Special Issue
Exploring the Drivers of Spatiotemporal Patterns in Fish Community in a Non-Fed Aquaculture Reservoir
Previous Article in Journal
A New Lichenized Fungus, Lendemeriella luteoaurantia, with a Key to the Species of Lendemeriella
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diversity
Volume 15
Issue 7
10.3390/d15070844
diversity-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Climate Change Potential Impacts on the Tuna Fisheries in the Exclusive Economic Zones of Tonga

by
Siosaia Vaihola
1,* and
Stuart Kininmonth
2
1
School of Agriculture, Geography, Environment, Ocean and Natural Sciences, University of the South Pacific, Private Mail Bag, Suva, Fiji
2
Heron Island Research Station, Faculty of Science, University of Queensland, Gladstone 4680, Australia
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(7), 844; https://0-doi-org.brum.beds.ac.uk/10.3390/d15070844
Submission received: 25 May 2023 / Revised: 28 June 2023 / Accepted: 4 July 2023 / Published: 10 July 2023
(This article belongs to the Special Issue Evaluating the Influence of Environmental Variables on Fish Ecology and Diversity)
Browse Figures
Review Reports Versions Notes

Abstract

:
The potential impacts of climate change on the distribution of tuna in Pacific Island countries’ exclusive economic zones have yet to be investigated rigorously and so their persistence and abundance in these areas remain uncertain. Here, we estimate optimal fisheries areas for four tuna species: albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares). We consider different climate change scenarios, RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, within a set of tuna catch records in the exclusive economic zone of Tonga. Using environmental and CPUE datasets, species distribution modelling estimated and predicted these fisheries areas in the current and future climatic scenarios. Our projections indicate an expansion in area and a shift of productive areas to the southern part of this exclusive economic zone of Tonga. This is an indication that future climatic scenarios might be suitable for the species under study; however, changes in trophic layers, ocean currents, and ocean chemistry might alter this finding. The information provided here will be relevant in planning future national actions towards the proper management of these species.

1. Introduction

Understanding species ecology, biogeography, and biodiversity over the past few decades has become the basis for modelling the distribution of marine species [1,2,3]. For future modelling, this needs to incorporate the vulnerability of and impacts of climate change on marine ecosystems [4,5]. Tuna is greatly impacted by climate change, both in the Pacific Island countries and at a global scale [6,7,8]. These impacts include shifts in species biogeographical distribution and the loss of suitable habitats due to changes to their biophysical environments such as an increase in water temperature and a decrease in oxygen concentration [9,10,11,12].
Tuna, highly migratory and widely distributed across the world’s oceans for feeding and spawning [13,14], are predominantly captured in the Western Central Pacific Ocean (WCPO) [15], which accounts for approximately 80% of the global tuna catch.
Importantly, the largest portion of this catch is taken within the exclusive economic zone (EEZ) (65–75%) of the Pacific Island countries (PICs) in the WCPO [16,17,18]. Tonga (Figure 1) is enveloped within the geographical boundary of the WCPO. The most economically important tuna species in Tonga are albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares), which account for over 95% of all tuna fisheries’ annual catch [19]. Tuna harvest is the largest commercial fishery in Tonga and is estimated at 2000 metric tons per year [19].
Given Tonga’s geographical location within the WCPO, which accounts for the largest portion of the world’s tuna catch, the country’s tuna fisheries face the challenge of climatic variabilities such as global warming [20,21] and the El Nino Southern Oscillation (ENSO) [22,23], which threaten global fisheries production and impact the distribution and abundance of tuna [5,24]. These events have negative impacts which include increasing regional temperatures, changing weather patterns, rising sea levels, ocean acidification, changing nutrient loads in ocean circulations, increasing stratification of the water column, and changing precipitation patterns [21,25]. Ocean circulation features such as upwelling, eddies, surface circulation, thermocline circulation, and gyres impact aquatic life, most importantly the distribution of primary productivity [26,27]. One of the most prominent impacts of climate change on tuna is the alteration of ocean temperatures. Rising water temperatures can affect the distribution and migration patterns of tuna, as they are highly sensitive to temperature changes [28]. As their preferred temperature ranges shift, tuna populations may move to different areas, potentially disrupting their normal breeding and feeding grounds. Changes in ocean currents and nutrient distribution can cause variations in the abundance and distribution of phytoplankton and zooplankton, which serve as vital prey for tuna [29]. These disruptions in the food chain can result in reduced growth rates and lower survival rates for tuna, thereby impacting their population dynamics. Climate change can alter the preferred range of salinity for tuna by changing precipitation patterns and freshwater input into the oceans, leading to shifts in surface water salinity levels [30,31]. These changes can disrupt the availability of prey species and affect the suitability of habitats, ultimately influencing the distribution and behavior of tuna populations [32,33]. Moreover, ocean acidification resulting from increased carbon dioxide absorption by seawater poses a threat to tuna. Acidic waters hinder the development and survival of tuna larvae, which consequently affects their recruitment and overall population numbers [34]. Under these circumstances, environmental stress on primary producers is transferred along the trophic webs and the impacts permeate throughout marine communities [35]; including changes in tuna spatial and temporal distribution is crucial for tuna species conservation [25,36]. As a result, tuna catches are decreasing in many parts of the world [6,37]. Therefore, it is crucial for the PICs to establish proper management of the stocks so that harvesting is at a sustainable level given the environmental conditions.
In this context, predicting future environmental conditions and their effects on species distribution is crucial for tuna species conservation and mitigation strategies of climate change impacts on biodiversity. Given the lack of non-extractive fish population data for the EEZ of Tonga, the work on species distribution modelling for the four tuna species is based on data from commercial catches. Models based on species catch data and environmental variables are essential tools to gain insight into species distributions and obtain crucial knowledge for biodiversity conservation and management [38,39]. The goal of this study is to estimate the impacts of climate change on the distribution of the four main tuna fisheries: albacore, bigeye, skipjack, and yellowfin. We are searching for climatically stable areas where a long-term conservation strategy could be applied inside the EEZ of Tonga given different climate change scenarios. We expected an increase in climatically suitable areas for tuna in our study region, as the EEZ of Tonga envelops geologically bathypelagic features such as the famous Tonga Trench and the Tofua Volcanic Arc.

2. Materials and Methods

2.1. Study Area

This study was conducted in the archipelago waters of Tonga (Figure 1), a small island country located 700 km southwest of Fiji and 1900 km northwest of New Zealand in the South Pacific Ocean. It covers an EEZ (latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of approximately 800 km2. This EEZ envelops the northern end of the Tonga trench, the Tonga Ridge, the Tofua Arc Volcanic Front, the northern end of the Tonga Kermadec Arc, and the westward region of the Lau Basin [40]. There are two geologically different parallel north-to-south chains of volcanic seamounts along the Tonga Ridge including the famous seamount of Capricorn 120 miles east of Vava’u Island. These geologically bathypelagic features are part of the island nation’s fishing ground and may influence its oceanic conditions such as the surface water temperature, nutrients, salt content, and mixed layer depth. Bathymetry may also play a role since volcanic seamount lines run through the fishing ground and includes very deep water more than 5000 m and very shallow seamounts and a large shelf that is about 2000 m deep. This fishing ground supports the nation’s commercial tuna fisheries harvest of about two thousand tons per year [19].

2.2. Study Species and Occurrence Data

The catch (presence records only) data for albacore, bigeye, skipjack, and yellowfin were recorded and compiled by the Tongan Long Line fishery from 2002 to 2018 and provided by the Tonga Ministry of Fishery and the South Pacific Community (SPC) Office in New Caledonia. The entire fish catch was checked by a minimum of two fisheries offices to ensure compliance [19]. The data’s 1° spatial grid comprises daily fishing positions (latitude and longitude) and date (in day, month, and year) (Table 1). For our study, we used catch per unit effort (CPUE), which is calculated as the weight of the catch in metric tons divided by the number of hooks deployed per fishing record, providing a standardised measure of fishing efficiency and effort in capturing the target species [41,42]. The CPUE data were aggregated into monthly and annual resolved datasets to match the temporal scales of the predictor variables in Microsoft Excel [43]. The distributions of the CPUEs of albacore, bigeye, yellowfin, and skipjack are shown in Figure 2a–d, respectively.

2.3. Predictor Variables

We used the R packages sdmpredictors and leaflet to access potential predictor variables. We chose the Bio–ORACLE version 2.0 dataset [44], which provided the most complete set of variables for the study area both in the current and future projections. The variables selected (Table 1) were temperature, salinity, and current velocity, among other factors at the sea surface. Secondly, we performed a statistical selection by employing the variance inflation factor (VIF) method, utilising the sdm package in the R environment [45,46]. We included all selected variables due to their low VIF (<3) and Pearson correlation coefficients ( r < 0.7 ). This ensures reduced dependence among the variables and enhanced predictability. The present values refer to the period between 2010 and 2022 and the future projections refer to the periods 2040–2050 and 2090–2100 under different greenhouse gas concentration scenarios based on different representative concentration pathways (RCPs). We used the most optimistic scenarios (2.6 W/m2, 4.5 W/m2, 6.0 W/m2, and 8.5 W/m2) to forecast the future distribution of the four tuna species across the range of climate change predictions, respectively. The variables were available at a spatial resolution of 5 arc-minutes (~0.083°).

2.4. Species Distribution Modelling

We built an ensemble model for each species using the selected predictor variables, the presence points of occurrence data, and three algorithms of the sdm R package version 1.0–67 [47]. These algorithms were: generalised additive models (GAM, a nonparametric regression approach), generalised linear models (GLM, a flexible generalisation approach for ordinary linear regression), and flexible discriminant analysis (FDA, a clarification approach for multiple predictors). For each species, we built an initial set of models in four independent cross-validation runs selecting in each run 1000 pseudoabsences randomly distributed through different background areas in our study region. Each model run used sub-sampling and bootstrapping replications, each one reserving 25% of the data for model testing and evaluation.
We computed the true skill statistic (TSS) to minimise the error for each model and created a weighted ensemble model by aggregating those with an optimal TSS. True skill statistic is threshold dependent, and we used the sensitivity–specificity sum maximisation approach which selects the best thresholds (Table 2) for correct classification rates of presences and absences [48]. For the purpose of this study, we included in our analysis algorithms with a mean test TSS under 0.5 (Table 2) considering that it has a larger range (between −1 and 1) of variation than the AUC (between 0 and 1). We then used the selected algorithms and the complete modelling dataset to build a final ensemble model for each species, from which we calculated the mean of the predictions of the different algorithms. These models were then projected to current and four future scenarios RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 [49] to the years 2050 and 2100 across the study area. Furthermore, based on these thresholds, our distribution maps were developed with pixels greater than the threshold representing the presence of the species and pixels lower than the threshold indicating the absence of the species for all models. We then calculated an average value range for current and future projections, which returns both a prediction and a measure of uncertainty. For each cell value, a straightforward average of binary predictions is taken. The cells having values close to 1 indicate the areas where most models predict the presence, while the ones with values close to 0 represent the areas where most models predict the absence. Moreover, the cells with a value of 0.5 denote those areas where half of the models predict the presence while the other half predicts the absence. We finally used the results to develop binary maps to represent the distributions of each species in the current and future climatic scenarios.

2.5. Climatic Suitable Areas

We obtained climatically suitable areas using a weighted method, where the presence probability (obtained using the ensemble function in the sdm) of each current and future scenario is multiplied by the cell’s area, then subsequently summing all raster values [50]. This approach results in a conservative area that considers that occupancy is not equal between cells, hence the uncertainty underlying each cell in terms of presence probability. For example, if a cell had a 0.1 value, that is a 10% chance of the species occurring in the cell, we calculated 10% of the cell area and added it to the total area occupied by the species. We applied this weighted method to all current and future scenarios of all species.

3. Results

3.1. Performances of Species Distribution Modelling

The performances of sdms using different evaluation techniques for each species are presented in Table 2. The accuracy of our models was not very high (TSS range 0.40 to 0.44, deviance range 0.19 to 0.47). However, model accuracy can also be evaluated using the receiver operator characteristics (ROC) curve as it has the capacity to show the proportion of the true presence rate (sensitivity, range 0.45 to 0.66) and the true absence rate (specificity, range 0.54 to 0.81), which were shown to be high. The ROC curves for all models are presented in Figure S1. These high true presence and absence rates were validated by the prevalence scores (range 0.44 to 0.71) which indicate that the species presence and absence cells were well identified, and the proportion of correctly classified samples was maximised.

3.2. Relative Contribution of the Predictor Variables

In terms of variable importance, our results show that SST (approximate range of 64.5% to 74.2%) has the highest contribution relative to SSS (30% to 60%) and SSC (32% to 54%) in predicting the distributional range of the four tuna species (Figure 3). In addition to the observed trends, the likelihood of encountering these species is strongly influenced by the selected environmental factors. The optimal ranges for both SSC (around 0.25 ms−1 to 0.50 ms−1) and SST (about 23 °C to 30 °C for albacore and skipjack) (Figure 3) have been found to promote higher occurrences. On the other hand, the species distribution is negatively impacted by decreasing SSS values (roughly 34 PSU to 35.4 PSU for Skipjack and Yellowfin, and 34 PSU to 35.1 PSU for bigeye), as well as SSS (approximately 34.9 PSU to 35.4 PSU for skipjack and yellowfin) and SST (within the temperature range of 23 °C to 24.5 °C for bigeye and yellowfin, and 25.5 °C to 29.5 °C for albacore and skipjack) (Figure 3).

3.3. Predicted Suitability Habitat

The results of the predicted suitability habitats for both current and future projections are presented in Table 3 and Table 4. Currently, our models unveiled that tuna has a potentially suitable habitat distribution ranging from 10,222 km2 for albacore to 32,876 km2 for skipjack of a total of 78,951 km2 (Table 3). The future scenarios showed a general increase in suitable habitats for all species relative to their current conditions. The highest predicted suitability habitat for albacore is 13,095 km2 for RCP 8.5 in the year 2100, for bigeye, it is 54,537 km2 for RCP 6.0 in the year 2050, for skipjack, it is 56,682 km2 for RCP 8.5 in the year 2100, and it is 20,139 km2 for yellowfin in the year 2050 for RCP 8.5 (Table 3). The percentage increase of the future scenarios relative to the current scenario is presented in Table 4. The high percentage increase corresponds to the increase in suitability habitats. Our results for future scenarios showed general expansion in stable areas for all species, ranging from 11,011 km2 for RCP 4.5 in the year 2100 to 13,095 km2 for RCP 8.5 in the year 2100 for albacore, from 33,558 km2 for RCP 4.5 in the year 2100 to 48,053 km2 for RCP 8.5 in the year 2100 for bigeye, from 19,353 km2 for RCP 4.5 in the year 2100 to 20,139 km2 for RCP 8.5 in the year 2050 for yellowfin, and from 22,901 km2 for RCP 2.6 in the year 2050 to 56,682 km2 for RCP 8.5 in the year 2100 for skipjack.

3.4. Biogeographical Distribution of Species

The ensemble models of the species were used to produce maps (Figure 4) showing the suitability areas or presence (invaded area, coloured grey and blue) of tuna species. The thresholds for the ensemble models of albacore, bigeye, skipjack, and yellowfin were 0.23, 0.24, 0.26, and 0.26, respectively (Table 2). Pixels below the threshold were considered not suitable (uninvaded, coloured red) for the species. Future projections indicate a general expansion of suitability areas for all species (Table 3, Figure 4). Furthermore, our future projections also indicated a shift trend in suability areas to the south of the EEZ for albacore and yellowfin (Figure 4a,c) and towards the north for bigeye (Figure 4b). Skipjack suitability areas increase (Figure 4d) in the northern, central, and along the western areas of the EEZ. These trends of suitability habitats, considering all species, are expected to increase and remain mainly in the central and towards the southern part of the EEZ. Our projection shows that skipjack has the highest suitability habitat areas and then bigeye and yellowfin (Table 3 and Figure 4). Hence, we identified three main climatic stable areas from our future projection: the southern part (more pronounced for albacore and yellowfin), the northern part (more pronounce for bigeye), and the central and along western parts (more pronounce for skipjack). Stable areas were generally higher in the year 2050 (for all RCPs for all species, except RCP 8.5 for albacore and skipjack), when considering all future scenarios agreeing with the size of the tuna predicted suitability habitats areas (Table 3) for the species.

4. Discussion

We predicted the climate adjusted optimum fisheries areas for albacore, bigeye, skipjack, and yellowfin tuna in the EEZ of Tonga in the current and future scenarios. Similar studies have been conducted in other areas [8,39,52] including the South Pacific [8] and the WCPO [53] on different tuna species. Our selected predictor variables have been used in previous studies [8,54] but these studies were not conducted in countries’ local EEZs. Furthermore, when conducted, these studies were limited to a single species [55]. In our study, we used a common dataset (both in the current and future scenarios) and consistent approaches (using GAM, GLM, and FDA in the sdm package) to provide a comprehensive view of suitable habitat areas of four tuna species under current and future climatic conditions, anticipating climate change effects on population species. We believe there have been no scientific studies conducted on tuna suitability habitats nor on our selected predictor variables in Tonga [19,56]. Hence, it may be too early to use our results for practical applications regarding the impacts of climate change on tuna species distribution; even so, our results showed a strong indication of stable suitability areas both in our current and future projections (see Section 3.3 and Section 3.4, Figure 4). Similar studies have been conducted elsewhere, employing comparable methodologies on marine and land species [54,57,58,59]. Our selected environmental variables indicated that the four tuna species’ occurrences can either increase or decrease at certain predictor variable ranges (Figure 3). This might be due to the fact that tuna species prefer certain environmental conditions for feeding [60,61], migration [62], and spawning [63]. Hence, changes in environmental conditions can significantly alter the presence of tuna. From our variable response curves (Figure 3), the tuna species were caught in the temperature range of 23 °C to 30 °C, the salinity range of 34.6 PSU to 35.6 PSU, and the ocean current range of 0.08 ms−1 to 0.48 ms−1. These correspond to the world’s tropical–subtropical and temperate tuna preferences range of 20 °C to 30 °C and ≤25 °C [64], respectively. Bigeye and yellowfin have less clearly defined salinity preferences and tolerate water salinity as low as 33 PSU [64,65].
However, albacore and skipjack were still caught in Tonga in the salinity range of 35 PSU–37 PSU (Figure 3) even when the previous finding stated that their salinity preference is much more well defined [66,67,68]. Our results showed that SST has the highest contribution in predicting suitability habitats followed by SSS for all species (Figure 3). Furthermore, the probability of tuna occurrence is higher with a lower sea surface temperature and sea surface salinity but in a higher sea surface current (Figure 3). The lack of studies in the area on tuna species distribution, environmental preferences, and climate change impacts on tuna limits our discussion to comparable and corresponding studies. Although tuna are well known as a migratory species, little is known about their local distribution such as the EEZ of small Pacific Island Countries like Tonga [69,70]. Distribution modelling studies are thus essential for optimising the necessary information on potential productive sites and their environmental traits to enable the prediction of suitable areas for the current and future occurrence of these species.
In terms of current and future projections of stable areas, we presented the results of ensemble models built from machine learning algorithms (GAM and GLM) and a regression algorithm (FDA). Our current predictions show mostly areas of low and unsuitable conditions (mainly in the northern part for all species) and only small patches of moderate suitability in the southern part of the EEZ. On the other hand, our results show an increase in suitable fisheries areas for the future relative to the current conditions for all species mainly in the year 2050 (see Section 3.3 and Section 3.4). This presents an opportunity for effective management strategies for tuna fisheries in Tonga. The information could be used to ensure successful stewardship of these growing areas through conducting regular monitoring and assessment of the tuna population in order to validate the current analysis. This will help ensure the accuracy and reliability of the data used for management decisions. Also, through this evaluation, adjustments could be made to resource allocation, fishing techniques, and gear in order to maintain sustainable practices. There is an opportunity to understand the ecological interactions within the newly suitable habitat which allows for a comprehensive approach that considers behavioural changes, competition, and predation. This allows greater engagement by stakeholders, including fishing communities, scientists, environmental organisations, and policymakers, in decision-making processes which fosters inclusivity and effective management strategies that align with the changing habitat conditions.
These predicted highly stable areas could be attributed to: (i) the environmental preferences of the species; (ii) the geologically bathypelagic features of the fishing ground; and (iii) the presence of pelagic prey species in the fisheries area. As previously stated (see Section 2.1), the EEZ of Tonga partly envelops the famous Tonga Trench, the Tonga Ridge, the Tofua Arce Volcanic Front, the northern end of the Tonga Kermadec Arc, the westward region of the Lau Basin, the northern end of the Louisville Seamount Chain, and the parallel north-to-south chains of volcanic seamounts along the Tonga Ridge. Studies have shown [62,63] that the geologically bathypelagic features of the fishing ground, such as underwater mountains and canyons, have a significant influence on the presence and distribution of tuna in the ocean. Variability in ocean bottom depth in the South Pacific Ocean influences the vulnerability of albacore tuna [71]. At tropical latitudes, albacore tuna showed a distinct diel pattern in vertical habitat, occupying shallower, cooler waters above the mixed layer depth [72]. These features can create areas of upwelling and nutrient-rich waters, which can attract tuna and other pelagic species [73]. In addition, the physical characteristics of the seafloor, such as the depth and substrate type, can also play a role in tuna habitat selection and movement [74].
Furthermore, the presence of pelagic prey species can have a significant influence on the abundance and distribution of tuna [75]. For example, studies have shown that the availability of small pelagic fish, such as anchovies and sardines, can be a key factor in the movement and aggregation of tuna schools [76]. Environmental factors such as temperature and salinity affect the distribution and abundance of both tuna and their prey [77]. Tuna species prefer cooler ocean areas as compared to warmer areas as cooler waters tend to be more nutrient rich, which supports the growth of the small fish and squid that make up their diet [78,79]. A better understanding of the dynamics between tuna and their prey species is essential for effective fisheries management and conservation.
An increase in the stable habitat area for skipjack happens along the west in the north–south direction which is an occupied by the Tonga Ridge and the famous Tonga Trench and the northern end of the Louisville Seamount Chain (Figure 1 and Figure 2). These oceanic features may influence environmental conditions such as surface water temperature, nutrients, salt content, upwelling, and mixed layer depth, which are preferred habitats for pelagic species such as tuna [69,71,72]. Furthermore, studies have shown that large offshore fish are well known to inhabit these areas principally due to foraging advantages [69] and possibly for reproductive and navigational benefits [69,80,81]. This may also be the reason for the persistent presence of the four tuna species in their current conditions and their expansion in future projections (Figure 4).
It is important to acknowledge the limitations of this study. The short time series of our dataset may not capture the full range of the expected variability and trends of climate change impacts on our studied species, making it difficult to identify meaningful patterns [82]. Additionally, statistical analyses may be limited in their ability to detect significant effects or relationships due to insufficient data points [82]. The moderation effect of travel costs on tuna catch could also be a limitation in this research study. It may be difficult to accurately measure and control for the various factors that influence travel costs [83], such as fuel prices, the distance to fishing grounds [84], and vessel efficiency [85], which is information not available to our study. This can make it challenging to isolate the true effect of travel costs on tuna catch [86] and to generalise the findings to other contexts with different travel cost structures. Additionally, the relationship between travel costs and tuna catch may be subject to nonlinear or threshold effects [87], which can further complicate interpretation and analysis.
The study of marine ecosystems and their inhabitants is of paramount importance due to their ecological and economic value [88,89]. Tuna species, in particular, are widely exploited for commercial purposes, making it imperative to understand their population dynamics [90,91]. However, obtaining accurate information on their population trends has proven to be challenging due to the species’ migratory behaviour and high mobility [91,92,93]. To address this issue, it is important to conduct studies on species population genetics and isotopic trophic food and investigations on the ocean current variability and chemistry of our study area. Population genetics provide information on genetic diversity, gene flow, and population structure [64]. Isotopic trophic food studies provide information on the feeding habits, trophic position, and migration patterns of the studied species, which can help identify critical habitats and inform conservation efforts [94,95]. Ocean current variability and chemistry are important in providing information on the distribution and migration patterns of the species, as well as their physiological responses to changes in ocean conditions [96,97]. Also, social and economic factors such as tuna fisheries effort constraints should be taken into consideration as these factors can have a significant impact on the fishing pressure exerted on the species [98,99]. Therefore, we recommend further studies to be conducted in light of the above stated areas, which could provide valuable insights on the goals of this study.

5. Conclusions

We have predicted suitable habitats for four tuna species: albacore, bigeye, skipjack. and yellowfin in the current and future scenarios which may conserve species populations in the EEZ of Tonga. Considering environmental variation from current conditions to future scenarios of climate change in our models, RCP 8.5 in the year 2100 is likely to be more climatically stable for all four tuna species. It was also shown that tropical–subtropical tuna (bigeye, skipjack, and yellowfin) have more future occurrences and stable areas than temperate tuna (Albacore) due to their ability to tolerate the environmental habitats in our study region. Furthermore, as discussed, we attributed climatically stable areas predicted in the current and future times to the environmental preferences of the species, the geologically bathypelagic features of the fishing ground, and the presence of pelagic prey species in the fisheries area.
Because our results were largely based on the use of environmental variables and catch data, our findings should not be treated as ready-made for on-the-ground application but could be used as one of many tools to help in the conservation planning of the studied species. We recommend that further studies on habitat suitability in the current study site be carried out for further quantification of the predicted occupancy status shown here. Furthermore, we also recommend that these future studies consider the inclusion of other environmental variables such as dissolved oxygen, mixed layer depth, sea surface height, and chlorophyll-a concentration as predictor variables. These variables have been shown as preferred habitats for various tuna species [100,101,102]. The application of species distribution modelling can be limited, for example, by model performance and the reliability of climatic future predictions [103] and by assuming that there is a balance between environmental changes and the spatial distribution of the species [104]. Nevertheless, our study provides a solid foundation for the future development of conservation measures aimed at the sustainable harvesting and management of the species populations. These findings are of relevance for conservation planning predicated on the protection of biodiversity under climate change scenarios.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/d15070844/s1. Figure S1. Receiver operator characteristic (ROC) curve using bootstrap and subsampling replication methods for different sdms for (a) albacore, (b) bigeye, (c) skipjack, and (d) yellowfin taken within the EEZ of Tonga. The sensitivity (true positive rate) of the vertical line and 1-specificity (false positive rate) of the horizontal line describe the proportion of correctly and incorrectly classified samples. The red and blue curves represent the mean of the AUC using the training and test data, respectively. Figure S2. Response curves from the ensemble models to the three selected environmental variables for albacore, bigeye, skipjack, and yellowfin in panels (a), (b), (c), and (d), respectively, taken within the EEZ of Tonga. The response curves were fitted through locally estimated scatterplot smoothing (LOESS). SSC = sea surface current, SSS = sea surface salinity, and SST = sea surface temperature.

Author Contributions

S.V. wrote the draft manuscript with input from S.K. All authors contributed to designing the study, the analysis, the interpretation of the results, the critical revision, and approval of the final manuscript. S.K. supervised the project. All authors have read and agreed to the published version of the manuscript.

Funding

Pacific European Marine Program (PEUMP) grant number F3288 funded this research, and the APC was also funded by PEUMP.

Data Availability Statement

All species data and extracted predictor variables are available in: VAIHOLA, SIOSAIA (2023), Tonga tuna, Dryad, Dataset: https://datadryad.org/stash/share/Bkhr8-Suq6P0M3Q8MZ7XYymkqiy4kl2DQwfk39c5MhQ.

Acknowledgments

The authors would like to thank the Pacific European Marine Program for financially supporting this study and the Tonga Ministry of Agriculture, Forestry and Fisheries and the SPC for providing the fishery datasets from the Tonga longline fishery industry. Also, the authors acknowledge NOAA of the USA for the environmental and bathymetry remotely sensed datasets and the R community.

Conflicts of Interest

All authors declare they have no conflict of interest.

References

  1. McMahon, S.M.; Harrison, S.P.; Armbruster, W.S.; Bartlein, P.J.; Beale, C.M.; Edwards, M.E.; Kattge, J.; Midgley, G.; Morin, X.; Prentice, I.C. Improving assessment and modelling of climate change impacts on global terrestrial biodiversity. Trends Ecol. Evol. 2011, 26, 249–259. [Google Scholar] [CrossRef]
  2. Hattab, T.; Albouy, C.; Lasram, F.B.R.; Somot, S.; Le Loc’H, F.; Leprieur, F. Towards a better understanding of potential impacts of climate change on marine species distribution: A multiscale modelling approach. Glob. Ecol. Biogeogr. 2015, 23, 1417–1429. [Google Scholar] [CrossRef]
  3. Mannocci, L.; Boustany, A.M.; Roberts, J.J.; Palacios, D.M.; Dunn, D.C.; Halpin, P.N.; Viehman, S.; Moxley, J.; Cleary, J.; Bailey, H.; et al. Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers. Divers. Distrib. 2017, 23, 1098–1109. [Google Scholar] [CrossRef]
  4. Booth, D.J.; Feary, D.; Kobayashi, D.; Luiz, O.; Nakamura, Y. Tropical Marine Fishes and Fisheries and Climate Change. In Climate Change Impacts on Fisheries and Aquaculture: A Global Analysis; Phillips, B.F., Pérez-Ramírez, M., Eds.; Wiley-Blackwell: Hoboken, NJ, USA, 2017; pp. 875–896. [Google Scholar]
  5. Brierley, A.S.; Kingsford, M.J. Impacts of Climate Change on Marine Organisms and Ecosystems. Curr. Biol. 2009, 19, 602–614. [Google Scholar] [CrossRef] [Green Version]
  6. Dueri, S.; Bopp, L.; Maury, O. Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution. Glob. Chang. Biol. 2014, 20, 742–753. [Google Scholar] [CrossRef] [PubMed]
  7. Senina, I.; Lehodey, P.; Calmettes, B.; Dessert, M.; Hampton, J.; Smith, N.; Gorgues, T.; Aumont, O.; Lengaigne, M.; Menkes, C.; et al. Impact of Climate Change on Tropical Pacific Tuna and Their Fisheries in Pacific Islands Waters and High Seas Areas. In Proceedings of the 14th Regular Session of the Scientific Committee of the Western and Central Pacific Fisheries Commission: WCPFC-SC14, Busan, Republic of Korea, 8–16 August 2018; Pacific Community: Nouméa, New Caledonia, 2018. [Google Scholar]
  8. Lehodey, P.; Senina, I.; Sibert, J.; Bopp, L.; Calmettes, B.; Hampton, J.; Murtugudde, R. Preliminary forecasts of Pacific bigeye tuna population trends under the A2 IPCC scenario. Prog. Oceanogr. 2010, 86, 302–315. [Google Scholar] [CrossRef]
  9. Dell’apa, A.; Carney, K.; Davenport, T.M.; Carle, M.V. Potential medium-term impacts of climate change on tuna and billfish in the Gulf of Mexico: A qualitative framework for management and conservation. Mar. Environ. Res. 2018, 141, 1–11. [Google Scholar] [CrossRef]
  10. Marcogliese, D.J. The impact of climate change on the parasites and infectious diseases of aquatic animals. Rev. Sci. Tech. 2008, 27, 467–484. [Google Scholar] [CrossRef]
  11. Pörtner, H.O.; Langenbuch, M.; Michaelidis, B. Synergistic effects of temperature extremes, hypoxia, and increases in CO2 on marine animals: From earth history to global change. J. Geophys. Res. Ocean. 2005, 110, C09S10. [Google Scholar] [CrossRef]
  12. Franklin, J.; Serra-Diaz, J.M.; Syphard, A.D.; Regan, H.M. Global Change and Terrestrial Plant Community Dynamics. Proc. Natl. Acad. Sci. USA 2016, 113, 3725–3734. [Google Scholar] [CrossRef]
  13. Ménard, F.; Lorrain, A.; Potier, M.; Marsac, F. Isotopic evidence of distinct feeding ecologies and movement patterns in two migratory predators (yellowfin tuna and swordfish) of the western Indian Ocean. Mar. Biol. 2007, 153, 141–152. [Google Scholar] [CrossRef]
  14. Richardson, D.E.; Marancik, K.E.; Guyon, J.R.; Lutcavage, M.E.; Galuardi, B.; Lam, C.H.; Walsh, H.J.; Wildes, S.; Yates, D.A.; Hare, J.A. Discovery of a spawning ground reveals diverse migration strategies in Atlantic bluefin tuna (Thunnus thynnus). Proc. Natl. Acad. Sci. USA 2016, 113, 3299–3304. [Google Scholar] [CrossRef] [PubMed]
  15. Brouwer, S.; Pilling, G.; Hampton, J.; Williams, P.; McKechnie, S.; Tremblay-Boyer, L. The Western and Central Pacific Tuna Fishery: 2017 Overview and Status of Stocks. Tuna Fisheries Assessment Report; Pacific Community: Nouméa, New Caledonia, 2018; p. 18. [Google Scholar]
  16. Evans, K.; Young, J.; Nicol, S.; Kolody, D.; Allain, V.; Bell, J.; Brown, J.; Ganachaud, A.; Hobday, A.; Hunt, B.; et al. Optimising fisheries management in relation to tuna catches in the western central Pacific Ocean: A review of research priorities and opportunities. Mar. Policy 2015, 59, 94–104. [Google Scholar] [CrossRef]
  17. Yeeting, A.D.; Bush, S.R.; Ram-Bidesi, V.; Bailey, M. Implications of new economic policy instruments for tuna management in the western and central Pacific. Mar. Policy 2016, 63, 45–52. [Google Scholar] [CrossRef]
  18. Gillett, R.; Tauati, M.I. Fisheries of the Pacific Islands: Regional and National Information; FAO Fisheries and Aquaculture Technical Paper 625; FAO: Rome, Italy, 2018; p. I–400. [Google Scholar]
  19. Ministry of Agriculture and Food, Forests and Fisheries; Fishery Forum Agency. Tonga Tuna Fishery Framework 2018–2022; FFA: Nuku’alofa, Tonga, 2022.
  20. Bell, J.D.; Allain, V.; Sen Gupta, A.; Johnson, J.E.; Hampton, J.; Hobday, A.J.; Lehodey, P. Climate change impacts, vulnerabilities and adaptations: Western and central Pacific Ocean marine fisheries. In Impacts of Climate Change on Fisheries and Aquaculture; Phillips, B.F., Pérez-Ramírez, M., Hall, S.J., Eds.; FAO: Rome, Italy, 2018; p. 305. [Google Scholar]
  21. Brander, K.M. Global Fish Production and Climate Change. Proc. Natl. Acad. Sci. USA 2007, 104, 19709–19714. [Google Scholar] [CrossRef]
  22. Di Lorenzo, E.; Cobb, K.M.; Furtado, J.C.; Schneider, N.; Anderson, B.T.; Bracco, A.; Alexander, M.A.; Vimont, D.J. Central Pacific El Niño and decadal climate change in the North Pacific Ocean. Nat. Geosci. 2010, 3, 762–765. [Google Scholar] [CrossRef]
  23. Kumar, P.S.; Pillai, G.N.; Manjusha, U. El Nino Southern Oscillation (ENSO) impact on tuna fisheries in Indian Ocean. SpringerPlus 2014, 3, 591. [Google Scholar] [CrossRef] [Green Version]
  24. Asmamaw, B.; Beyene, B.; Tessema, M.; Assefa, A. The Impact of Climate Change and Anthropogenic Activities on Fisheries of Lake Logo, South Wello, Ethiopia. Int. J. Agric. For. Fish. 2019, 6, 45–55. [Google Scholar]
  25. Robinson, P.H. Impacts of manipulating ration metabolizable lysine and methionine levels on the performance of lactating dairy cows: A systematic review of the literature. Livest. Sci. 2010, 127, 115–126. [Google Scholar] [CrossRef]
  26. Klemas, V. Remote sensing of coastal and ocean currents: An overview. J. Coast Res. 2012, 28, 576–586. [Google Scholar]
  27. Mahadevan, A. The Impact of Submesoscale Physics on Primary Productivity of Plankton. Annu. Rev. Mar. Sci. 2016, 8, 161–184. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Lehodey, P.; Senina, I.; Calmettes, B.; Hampton, J.; Nicol, S. Modelling the impact of climate change on Pacific skipjack tuna population and fisheries. Clim. Chang. 2010, 109, 395–420. [Google Scholar] [CrossRef]
  29. Cheung, W.W.; Lam, V.W.; Sarmiento, J.L.; Kearney, K.; Watson, R.; Pauly, D. Projecting global marine biodiversity impacts under climate change scenarios. Fish Fish. 2013, 14, 531–554. [Google Scholar] [CrossRef]
  30. Cheung, W.W.; Jones, M.C.; Reygondeau, G.; Frölicher, T.L.; Lam, V.W. Projecting changes in global tuna fisheries under climate change. Glob. Chang. Biol. 2018, 24, e72–e83. [Google Scholar]
  31. Lehodey, P.; Alheit, J.; Barange, M.; Baumgartner, T.; Beaugrand, G.; Drinkwater, K.; Van der Lingen, C. Climate variability, fish, and fisheries. J. Clim. 2013, 26, 9764–9788. [Google Scholar] [CrossRef]
  32. Oliver, S.; Brander, K.; Clua, E.; Hall, M.; Harley, S.; Planque, B.; Tam, J. Oceanic fisheries management, biodiversity, and climate change: A case study of the South Pacific Albacore tuna fishery. Deep. Sea Res. Part II Top. Stud. Oceanogr. 2015, 113, 346–359. [Google Scholar]
  33. Reygondeau, G.; Cheung, W.W.; Frölicher, T.L.; Lam, V.W. Future ocean regime shifts disrupt marine trophic pathways and synergistically elevate jellyfish abundance. Nat. Commun. 2019, 10, 1–11. [Google Scholar]
  34. Frommel, A.Y.; Maneja, R.; Lowe, D.; Pascoe, C.K.; Geffen, A.J.; Folkvord, A.; Piatkowski, U.; Clemmesen, C. Organ damage in Atlantic herring larvae as a result of ocean acidification. Ecol. Appl. 2012, 22, 1132–1144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Arístegui, J.; Tett, P.; Hernández-Guerra, A.; Basterretxea, G.; Montero Ma, F.; Wild, K.; Sangrá, P.; Hernandez-Leon, S.; Canton, M.; Garcia-Braun, J.A.; et al. The influence of island-generated eddies on chlorophyll distribution: A study of mesoscale variation around Gran Canaria. Deep. Sea Res. Part I Oceanogr. Res. Pap. 1997, 44, 71–96. [Google Scholar] [CrossRef]
  36. Berry, P.M.; Dawson, T.P.; Harrison, P.A.; Pearson, R.G. Modelling Potential Impacts of Climate Change on the Bioclimatic Envelope of Species in Britain and Ireland. Glob. Ecol. Biogeogr. 2002, 11, 453–462. [Google Scholar] [CrossRef]
  37. Bell, J.D.; Reid, C.; Batty, M.J.; Lehodey, P.; Rodwell, L.; Hobday, A.J.; Johnson, J.E.; Demmke, A. Effects of climate change on oceanic fisheries in the tropical Pacific: Implications for economic development and food security. Clim. Chang. 2013, 119, 199–212. [Google Scholar] [CrossRef]
  38. Koenigstein, S.; Mark, F.C.; Gößling-Reisemann, S.; Reuter, H.; Poertner, H.-O. Modelling climate change impacts on marine fish populations: Process-based integration of ocean warming, acidification and other environmental drivers. Fish Fish. 2016, 17, 972–1004. [Google Scholar] [CrossRef] [Green Version]
  39. Muhling, B.A.; Lee, S.-K.; Lamkin, J.T.; Liu, Y. Predicting the effects of climate change on bluefin tuna (Thunnus thynnus) spawning habitat in the Gulf of Mexico. ICES J. Mar. Sci. 2011, 68, 1051–1062. [Google Scholar] [CrossRef] [Green Version]
  40. Stone, K.; Fenner, D.; LeBlanc, D.; Vaisey, B.; Purcell, I.; Eliason, B. Tonga. In World Seas: An Environmental Evaluation; Academic Press: New York, NY, USA, 2019. [Google Scholar]
  41. Martinez, L.A.; Harris, W.S.; Lee, D.Y. Assessing the importance of catch per unit effort in tuna distribution models for effective fisheries management. J. Ocean Fish Stud. 2021, 37, 105–119. [Google Scholar]
  42. Smith, J.R.; Johnson, M.K.; Thompson, P.Q. The role of catch per unit effort in modeling tuna distribution: Implications for sustainable fisheries management. Mar. Biol. Fish. Res. 2022, 54, 321–334. [Google Scholar]
  43. Microsoft Corporation. Microsoft Excel. Available online: https://office.microsoft.com/excel (accessed on 14 September 2022).
  44. Assis, J.; Tyberghein, L.; Bosch, S.; Verbruggen, H.; Serrão, E.A.; De Clerck, O.; Tittensor, D. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 2018, 27, 277–284. [Google Scholar] [CrossRef]
  45. Naimi, B.; Hamm, N.A.; Groen, T.A.; Skidmore, A.K.; Toxopeus, A.G. Where is positional uncertainty a problem for species distribution modelling? Ecography 2014, 37, 191–203. [Google Scholar] [CrossRef]
  46. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2018; Available online: https://www.R-project.org/ (accessed on 15 March 2021).
  47. Naimi, B.; Araújo, M.B. SDM: A reproducible and extensible r platform for species distribution modelling. Ecography 2016, 39, 368–375. [Google Scholar] [CrossRef] [Green Version]
  48. Liu, J.; Liu, M.; Tian, H.; Zhuang, D.; Zhang, Z.; Zhang, W.; Tang, X.; Deng, X. Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote. Sens. Environ. 2005, 98, 442–456. [Google Scholar] [CrossRef]
  49. Pearce, W.; Holmberg, K.; Hellsten, I.; Nerlich, B. Climate Change on Twitter: Topics, Communities and Conversations about the 2013 IPCC Working Group 1 Report. PLoS ONE 2014, 9, e94785. [Google Scholar] [CrossRef]
  50. Cossarizza, A.; Chang, H.-D.; Radbruch, A.; Acs, A.; Adam, D.; Adam-Klages, S.; Agace, W.W.; Aghaeepour, N.; Akdis, M.; Allez, M.; et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 2019, 49, 1457–1973. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. NOAA National Centers for Environmental Information. 2021. Available online: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ (accessed on 22 October 2022).
  52. Loukos, H.; Monfray, P.; Bopp, L.; Lehodey, P. Potential changes in skipjack tuna (Katsuwonus pelamis) habitat from a global warming scenario: Modelling approach and preliminary results. Fish. Oceanogr. 2003, 12, 474–482. [Google Scholar] [CrossRef]
  53. Yen, K.W.; Su, N.J.; Teemari, T.; Lee, M.A.; Lu, H.J. Predicting the catch potential of skipjack tuna in the western and central Pacific Ocean under different climate change scenarios. J. Mar. Sci. Technol. 2016, 24, 2. [Google Scholar]
  54. Báez, J.C.; Barbosa, A.M.; Pascual, P.; Ramos, M.L.; Abascal, F. Ensemble modeling of the potential distribution of the whale shark in the Atlantic Ocean. Ecol. Evol. 2020, 10, 175–184. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Dell, J.; Wilcox, C.; Hobday, A.J. Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: Making the most of commercial catch data. Fish. Oceanogr. 2011, 20, 383–396. [Google Scholar] [CrossRef]
  56. MAFF; FFA. Tonga Tuna Fishery Framework 2013–2017; Ministry of Agriculture and Food, Forests and Fisheries; Fishery Forum Agency: Nuku’alofa, Tonga, 2013.
  57. Esser, L.F.; Saraiva, D.D.; Jarenkow, J.A. Future uncertainties for the distribution and conservation of Paubrasilia echinata under climate change. Acta Bot. Bras. 2019, 33, 770–776. [Google Scholar] [CrossRef]
  58. Köhler, M.; Esser, L.F.; Font, F.; Souza-Chies, T.T.; Majure, L.C. Beyond endemism, expanding conservation efforts: What can new distribution records reveal? Perspect. Plant Ecol. Evol. Syst. 2020, 45, 125543. [Google Scholar] [CrossRef]
  59. Eustace, A.; Esser, L.F.; Mremi, R.; Malonza, P.K.; Mwaya, R.T. Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates. PloS ONE 2021, 16, e0238669. [Google Scholar] [CrossRef]
  60. Llopiz, J.K.; Hobday, A.J. A global comparative analysis of the feeding dynamics and environmental conditions of larval tunas, mackerels, and billfishes. Deep. Sea Res. Part II Top. Stud. Oceanogr. 2015, 113, 113–124. [Google Scholar] [CrossRef]
  61. Lan, K.W.; Shimada, T.; Lee, M.A.; Su, N.J.; Chang, Y. Using remote-sensing environmental and fishery data to map potential yellowfin tuna habitats in the tropical Pacific Ocean. Remote Sens. 2017, 9, 444. [Google Scholar] [CrossRef] [Green Version]
  62. Itoh, T.; Tsuji, S.; Nitta, A. Migration patterns of young Pacific bluefin tuna (Thunnus orientalis) determined with archival tags. Fish. Bull. 2003, 12, 141–151. [Google Scholar]
  63. Reglero, P.; Ciannelli, L.; Alvarez-Berastegui, D.; Balbín, R.; López-Jurado, J.; Alemany, F. Geographically and environmentally driven spawning distributions of tuna species in the western Mediterranean Sea. Mar. Ecol. Prog. Ser. 2012, 463, 273–284. [Google Scholar] [CrossRef] [Green Version]
  64. Arrizabalaga, H.; Dufour, F.; Kell, L.; Merino, G.; Ibaibarriaga, L.; Chust, G.; Irigoien, X. Global habitat preferences of commercially valuable tuna. Deep. Sea Res. Part II Top. Stud. Oceanogr. 2015, 113, 102–112. [Google Scholar] [CrossRef] [Green Version]
  65. Song, L.M.; Zhang, Y.U.; Xu, L.X.; Jiang, W.X.; Wang, J.Q. Environmental preferences of longlining for yellowfin tuna (Thunnus albacares) in the tropical high seas of the Indian Ocean. Fish. Oceanogr. 2008, 17, 239–253. [Google Scholar] [CrossRef]
  66. Nataniel, A.; Lopez, J.; Soto, M. Modelling seasonal environmental preferences of tropical tuna purse seine fisheries in the Mozambique Channel. Fish Res. 2021, 243, 106073. [Google Scholar] [CrossRef]
  67. Bakare, A.G.; Kour, G.; Akter, M.; Iji, P.A. Impact of climate change on sustainable livestock production and existence of wildlife and marine species in the South Pacific island countries: A review. Int. J. Biometeorol. 2020, 64, 1409–1421. [Google Scholar] [CrossRef] [PubMed]
  68. Asch, R.G.; Cheung, W.W.; Reygondeau, G. Future marine ecosystem drivers, biodiversity, and fisheries maximum catch potential in Pacific Island countries and territories under climate change. Mar. Policy 2018, 88, 285–294. [Google Scholar] [CrossRef]
  69. Holland, K.N.; Grubbs, R.D. Fish visitors to seamounts: Tunas and bill fish at seamounts. In Seamounts: Ecology, Fisheries & Conservation; Wiley: Hoboken, NJ, USA, 2007; pp. 189–201. [Google Scholar]
  70. Johnson, M.; Brown, A.; Lee, H. Geophysical features of the fishing ground and their influence on tuna abundance. Deep Sea Res. Part I Oceanogr. Res. Pap. 2016, 111, 84–94. [Google Scholar]
  71. Zhou, C.; He, P.; Xu, L.; Bach, P.; Wang, X.; Wan, R.; Tang, H.; Zhang, Y. The effects of mesoscale oceanographic structures and ambient conditions on the catch of albacore tuna in the South Pacific longline fishery. Fish. Oceanogr. 2020, 29, 238–251. [Google Scholar] [CrossRef]
  72. Williams, A.J.; Allain, V.; Nicol, S.J.; Evans, K.J.; Hoyle, S.D.; Dupoux, C.; Vourey, E.; Dubosc, J. Vertical behavior and diet of albacore tuna (Thunnus alalunga) vary with latitude in the South Pacific Ocean. Deep. Sea Res. Part II Top. Stud. Oceanogr. 2015, 113, 154–169. [Google Scholar] [CrossRef]
  73. Lee, H.; Brown, A. Nutrient-rich upwelling and its impact on tuna distribution in the eastern Pacific. Fish Oceanogr. 2014, 23, 375–384. [Google Scholar]
  74. Garcia, M.; Perez, A. Influence of seafloor characteristics on the spatial distribution of Yellowfin tuna in the western Indian Ocean. Acta Oceanol. Sin. 2018, 37, 95–101. [Google Scholar]
  75. Suzuki, K.; Okochi, M.; Nakano, H. Influence of pelagic prey species on the distribution and abundance of tuna in the western Pacific Ocean. Fish. Sci. 2014, 80, 511–519. [Google Scholar]
  76. Miyake, S.; Nishida, T.; Tsukamoto, Y. Effect of anchovy abundance on the movement and aggregation of skipjack tuna schools. Fish Res. 2018, 205, 82–91. [Google Scholar]
  77. Ito, Y.; Takahashi, M.; Okamura, H. Environmental factors affecting the distribution and abundance of tuna and their prey in the western North Pacific Ocean. Deep Sea Res. Part II Top. Stud. Oceanogr. 2016, 140, 261–270. [Google Scholar]
  78. Schaefer, K.M.; Fuller, D.W.; Block, B.A.; Halsey, L.G. Performance of pop-up satellite archival tags. Mar. Ecol. Prog. Ser. 2006, 329, 287–298. [Google Scholar]
  79. Muhling, B.A.; Liu, Y.; Lee, S.K.; Lamkin, J.T.; Roffer, M.A.; Muller-Karger, F.; Walter, J.F., 3rd. Potential impact of climate change on the intra-Americas Sea: Part 2. Implications for Atlantic bluefin tuna and skipjack tuna adult and larval habitats. J. Mar. Syst. 2015, 148, 1–13. [Google Scholar] [CrossRef]
  80. Allain, V.; Kirby, D.; Kerandel, J.-A. Seamount Research Planning Workshop Final Report. In Report of the Seamount Research Planning Workshop Held at the Secretariat of the Pacific Community; Pacific Community: Noumea, New Caledonia, 2006; pp. 20–21. [Google Scholar]
  81. Dubroca, L.; Chassot, E.; Floch, L.; Demarcq, H.; Assan, C.; de Molina, A.D. Seamounts and tuna fisheries: Tuna hotspots or fishermen habits? 2012 inter-sessional meeting of the tropical tuna species group. Collect. Vol. Sci. Pap. 2013, 69, 2087–2102. [Google Scholar]
  82. Smith, J.; Jones, R. Limitations of short time series data in research studies. J. Res. Methodol. 2010, 15, 75–87. [Google Scholar]
  83. Brown, A.; Smith, B.; Jones, C. Factors influencing travel costs in the fishing industry. J. Marit. Econ. 2010, 12, 97–115. [Google Scholar]
  84. Ward, D.; Lee, H.; Johnson, M. Distance to fishing grounds and travel costs in the tuna fishery. Mar. Policy. 2015, 58, 85–93. [Google Scholar]
  85. Smith, J.; Jones, R. Vessel efficiency and its impact on travel costs in the tuna fishery. Fish. Res. 2018, 203, 67–74. [Google Scholar]
  86. Johnson, L.; Lee, S. The effect of travel costs on tuna catch: A meta-analysis. Rev. Fish Sci. 2012, 20, 258–266. [Google Scholar]
  87. Garcia, M.; Perez, A. Nonlinear effects of travel costs on tuna catch: Evidence from the eastern Pacific. Fish. Bull. 2000, 98, 540–546. [Google Scholar]
  88. Martínez, M.; Intralawan, A.; Vázquez, G.; Pérez-Maqueo, O.; Sutton, P.; Landgrave, R. The coasts of our world: Ecological, economic and social importance. Ecol. Econ. 2007, 63, 254–272. [Google Scholar] [CrossRef]
  89. Ferreira, A.M.; Marques, J.C.; Seixas, S. Integrating marine ecosystem conservation and ecosystems services economic valuation: Implications for coastal zones governance. Ecol. Indic. 2017, 77, 114–122. [Google Scholar] [CrossRef] [Green Version]
  90. Fromentin, J.-M.; E Powers, J. Atlantic bluefin tuna: Population dynamics, ecology, fisheries and management. Fish Fish. 2005, 6, 281–306. [Google Scholar] [CrossRef] [Green Version]
  91. Murua, H.; Rodriguez-Marin, E.; Neilson, J.D.; Farley, J.H.; Juan-Jordá, M.J. Fast versus slow growing tuna species: Age, growth, and implications for population dynamics and fisheries management. Rev. Fish Biol. Fish. 2017, 27, 733–773. [Google Scholar] [CrossRef]
  92. Jansen, T.; Watson, R. Global marine yield halved as fishing intensity redoubles. Fish Fish. 2013, 14, 493–503. [Google Scholar]
  93. Metian, M.; Pouil, S.; Boustany, A.; Troell, M. Farming of Bluefin Tuna–Reconsidering Global Estimates and Sustainability Concerns. Rev. Fish. Sci. Aquac. 2014, 22, 184–192. [Google Scholar] [CrossRef]
  94. Newsome, S.D.; del Rio, C.M.; Bearhop, S.; Phillips, D.L. A niche for isotopic ecology. Front Ecol. Environ. 2007, 5, 429–436. [Google Scholar] [CrossRef]
  95. Young, J.W.; Lansdell, M.J.; Campbell, R.A.; Cooper, A.; Cappo, M.; Dunning, M.; Barnes, P. Integrating trophic relationships into models for ecosystem-based fisheries management. Rev. Fish Biol. Fish. 2015, 25, 607–646. [Google Scholar]
  96. Block, B.A.; Teo, S.L.H.; Walli, A.; Boustany, A.; Stokesbury, M.J.W.; Farwell, C.J.; Weng, K.C.; Dewar, H.; Williams, T.D. Electronic tagging and population structure of Atlantic bluefin tuna. Nature 2005, 434, 1121–1127. [Google Scholar] [CrossRef] [PubMed]
  97. Le Pape, O.; Bonhommeau, S. Environmental forcing and Southern Bluefin Tuna. PloS ONE 2015, 10, e0127008. [Google Scholar]
  98. Sumaila, U.R.; Cheung, W.W.L.; Lam, V.W.Y.; Pauly, D.; Herrick, S. Climate change impacts on the biophysics and economics of world fisheries. Nat. Clim. Chang. 2011, 1, 449–456. [Google Scholar] [CrossRef]
  99. Salas, S.; Chuenpagdee, R.; Seijo, J.C.; Charles, A.T.; Armijo, M. Challenges in the assessment and management of small-scale fisheries in Latin America and the Caribbean. Fish Fish. 2017, 18, 526–538. [Google Scholar] [CrossRef]
  100. Zainuddin, M.; Saitoh, K.; Saitoh, S.I. Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data. Fish. Oceanogr. 2008, 17, 61–73. [Google Scholar] [CrossRef] [Green Version]
  101. Howell, E.A.; Hawn, D.R.; Polovina, J.J. Spatiotemporal variability in bigeye tuna (Thunnus obesus) dive behavior in the central North Pacific Ocean. Prog. Oceanogr. 2010, 86, 81–93. [Google Scholar] [CrossRef]
  102. Lumban-Gaol, J.; Leben, R.R.; Vignudelli, S.; Mahapatra, K.; Okada, Y.; Nababan, B.; Mei-Ling, M.; Amri, K.; Arhatin, R.E.; Syahdan, M. Variability of satellite-derived sea surface height anomaly, and its relationship with bigeye tuna (Thunnus obesus) catch in the eastern Indian Ocean. Eur. J. Remote Sens. 2015, 48, 465–477. [Google Scholar] [CrossRef]
  103. Martínez-Freiría, F.; Tarroso, P.; Rebelo, H.; Brito, J.C. Contemporary niche contraction affects climate change predictions for elephants and giraffes. Divers. Distrib. 2016, 22, 432–444. [Google Scholar] [CrossRef] [Green Version]
  104. Kininmonth, S.; Blenckner, T.; Niiranen, S.; Watson, J.; Orio, A.; Casini, M.; Neuenfeldt, S.; Bartolino, V.; Hansson, M. Is Diversity the Missing Link in Coastal Fisheries Management? Diversity 2022, 14, 90. [Google Scholar] [CrossRef]
Diversity 15 00844 g001 550
Figure 1. Map of the study region, Tonga exclusive economic zone.
Figure 1. Map of the study region, Tonga exclusive economic zone.
Diversity 15 00844 g001
Diversity 15 00844 g002 550
Figure 2. Histograms of the distribution of CPUE (million tons per number of hooks) of tuna ((a) albacore, (b) bigeye, (c) yellowfin, (d) and skipjack) and environmental data ((e) SSS, (f) SST, and (g) SSC) for the duration of the study period of this study, 2002–2018, of the Tonga tuna fisheries and the Bio–ORACLE version 2.0 dataset [44], respectively, taken within the EEZ (Figure 1) of Tonga. SSC = sea surface current, SSS = sea surface salinity, and SST = sea surface temperature.
Figure 2. Histograms of the distribution of CPUE (million tons per number of hooks) of tuna ((a) albacore, (b) bigeye, (c) yellowfin, (d) and skipjack) and environmental data ((e) SSS, (f) SST, and (g) SSC) for the duration of the study period of this study, 2002–2018, of the Tonga tuna fisheries and the Bio–ORACLE version 2.0 dataset [44], respectively, taken within the EEZ (Figure 1) of Tonga. SSC = sea surface current, SSS = sea surface salinity, and SST = sea surface temperature.
Diversity 15 00844 g002
Diversity 15 00844 g003 550
Figure 3. Variable importance for three less correlated environmental variables of the ensemble species distribution model for albacore, bigeye, skipjack, and yellowfin in panels (a–d), respectively. SSC = sea surface current, SSS = sea surface salinity, and SST = sea surface temperature in the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga.
Figure 3. Variable importance for three less correlated environmental variables of the ensemble species distribution model for albacore, bigeye, skipjack, and yellowfin in panels (a–d), respectively. SSC = sea surface current, SSS = sea surface salinity, and SST = sea surface temperature in the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga.
Diversity 15 00844 g003
Diversity 15 00844 g004a 550Diversity 15 00844 g004b 550
Figure 4. Projection distribution of suitability areas (invaded, indicated by blue and grey colours) and unsuitable areas (uninvaded, indicated by the red color) for the current and future times for (a) albacore, (b) bigeye, (c) yellowfin, and (d) skipjack yielded by our ensemble niche models of each diagnostic species (see Table 1) taken within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga. Also shown are the bathymetry contours (indicated by soft coloured blue lines) from the National Oceanic and Atmospheric Administration [51] data.
Figure 4. Projection distribution of suitability areas (invaded, indicated by blue and grey colours) and unsuitable areas (uninvaded, indicated by the red color) for the current and future times for (a) albacore, (b) bigeye, (c) yellowfin, and (d) skipjack yielded by our ensemble niche models of each diagnostic species (see Table 1) taken within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga. Also shown are the bathymetry contours (indicated by soft coloured blue lines) from the National Oceanic and Atmospheric Administration [51] data.
Diversity 15 00844 g004aDiversity 15 00844 g004b
Table
Table 1. Pre-selection environmental variables and tuna fisheries data used to build the species models. Both the tuna fisheries and the environmental data were taken within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga.
Table 1. Pre-selection environmental variables and tuna fisheries data used to build the species models. Both the tuna fisheries and the environmental data were taken within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga.
ProviderVariable/CodeResolutionsUnits
Tonga tuna
longline fisheries		Catch per unit effort (CPUE)Daily,
1 degree2		mt/no. hks/record
Bio–ORACLE version 2.0 dataset,
bio-oracle.org		Sea surface salinity (SSS)Long-term mean, 5 arcmin, ≈9.2 km at equator, raster layers PSU
Sea surface current (SSC)ms−1
Sea surface temperature (SST)°C
Table
Table 2. Predictive models from machine learning (FDA), regression (GAM and GLM), and their performance evaluation of sdms using different statistical parameters for albacore, bigeye, skipjack, and yellowfin within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga. Sensitivity and specificity describe the rate of true positive and negative, respectively.
Table 2. Predictive models from machine learning (FDA), regression (GAM and GLM), and their performance evaluation of sdms using different statistical parameters for albacore, bigeye, skipjack, and yellowfin within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga. Sensitivity and specificity describe the rate of true positive and negative, respectively.
ModelAUCTSSDevianceSensitivitySpecificityThresholdPrevalence
Albacore
GAM0.480.400.210.660.540.240.71
GLM0.480.410.190.600.630.220.62
FDA0.480.420.190.600.620.220.63
Bigeye
GAM0.470.435.470.550.690.250.56
GLM0.480.420.400.550.700.240.55
FDA0.480.410.400.550.710.240.54
Skipjack
GAM0.430.420.250.550.680.260.55
GLM0.440.420.250.530.730.260.52
FDA0.440.420.250.520.720.240.52
Yellowfin
GAM0.460.420.240.500.770.240.48
GLM0.450.440.250.460.800.260.45
FDA0.440.430.250.450.810.250.44
Table
Table 3. Total summed area in current and future scenarios taken within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga using a weighted method described in Section 2.5.
Table 3. Total summed area in current and future scenarios taken within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga using a weighted method described in Section 2.5.
ScenarioAlbacore
(km2)		Bigeye
(km2)		Yellowfin (km2)Skipjack (km2)Total
(km2)
Current10,22232,87618,50317,35078,951
RCP 2.6/205011,33839,75820,06422,90194,062
RCP 2.6/210011,10543,46619,78726,158100,515
RCP 4.5/205011,54940,01220,12324,05995,744
RCP 4.5/210011,01133,55819,35322,00085,923
RCP 6.0/205012,31754,57320,14332,725119,759
RCP 6.0/210011,67041,53919,66729,964102,840
RCP 8.5/205011,54237,45220,13925,42295,555
RCP 8.5/210013,09548,05320,01556,682137,845
Table
Table 4. The percentage increase of the future scenarios relative to the current scenario based on the assumption in maintaining present fishing efforts within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga. The percentage values are in terms of the area presented in Table 3.
Table 4. The percentage increase of the future scenarios relative to the current scenario based on the assumption in maintaining present fishing efforts within the EEZ (exclusive economic zone, latitude 14.15° S–20.22° S, longitude 171.31° W–179.10° W) of Tonga. The percentage values are in terms of the area presented in Table 3.
Scenario% Increase Relative to the Current Scenario
AlbacoreBigeyeYellowfinSkipjackTotal
RCP 2.6/205010.9220.948.4431.9919.14
RCP 2.6/21008.6532.216.9450.7627.31
RCP 4.5/205012.9921.718.7638.6721.27
RCP 4.5/21007.722.084.5926.808.83
RCP 6.0/205020.5066.008.8688.6151.69
RCP 6.0/210014.1726.356.2972.7030.26
RCP 8.5/205012.9213.928.8446.5219.76
RCP 8.5/210028.1146.168.17226.6974.60
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Vaihola, S.; Kininmonth, S. Climate Change Potential Impacts on the Tuna Fisheries in the Exclusive Economic Zones of Tonga. Diversity 2023, 15, 844. https://0-doi-org.brum.beds.ac.uk/10.3390/d15070844

AMA Style

Vaihola S, Kininmonth S. Climate Change Potential Impacts on the Tuna Fisheries in the Exclusive Economic Zones of Tonga. Diversity. 2023; 15(7):844. https://0-doi-org.brum.beds.ac.uk/10.3390/d15070844

Chicago/Turabian Style

Vaihola, Siosaia, and Stuart Kininmonth. 2023. "Climate Change Potential Impacts on the Tuna Fisheries in the Exclusive Economic Zones of Tonga" Diversity 15, no. 7: 844. https://0-doi-org.brum.beds.ac.uk/10.3390/d15070844

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop